From c5ce1cc0f16d667be3a239102969e5cda499f5d6 Mon Sep 17 00:00:00 2001 From: alecadair Date: Mon, 23 Dec 2024 22:27:14 +0200 Subject: [PATCH 1/2] changed license to apache2.0 --- .../current_mirror_ota_optimization/LICENSE | 876 ++++-------------- .../current_mirror_ota_optimization/README.md | 2 +- 2 files changed, 203 insertions(+), 675 deletions(-) diff --git a/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/LICENSE b/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/LICENSE index e72bfdda..ada0a48a 100644 --- a/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/LICENSE +++ b/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/LICENSE @@ -1,674 +1,202 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2024] [Alec Stefan Adair] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/README.md b/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/README.md index 52e3e3ca..5c774a13 100644 --- a/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/README.md +++ b/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/README.md @@ -205,4 +205,4 @@ make veryclean │ └── COPYRIGHT.txt ``` ## License -This project is licensed under the GPL 3.0 License. See LICENSE for more details. \ No newline at end of file +This project is licensed under the Apache 2.0 License. See LICENSE for more details. \ No newline at end of file From 06693dd96b74b4451910924dae87f3a5c44e5efa Mon Sep 17 00:00:00 2001 From: alecadair Date: Tue, 24 Dec 2024 00:37:18 +0200 Subject: [PATCH 2/2] changed license from GPL3.0 to apache2.0 --- ...t_mirror_ota_optimization-checkpoint.ipynb | 3279 ----------------- .../current_mirror_ota_optimization/LICENSE | 2 +- .../current_mirror_ota_optimization.ipynb | 111 +- .../images/ac_simulation_plot.svg | 166 +- 4 files changed, 113 insertions(+), 3445 deletions(-) delete mode 100644 ISSCC25/submitted_notebooks/current_mirror_ota_optimization/.ipynb_checkpoints/current_mirror_ota_optimization-checkpoint.ipynb diff --git a/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/.ipynb_checkpoints/current_mirror_ota_optimization-checkpoint.ipynb b/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/.ipynb_checkpoints/current_mirror_ota_optimization-checkpoint.ipynb deleted file mode 100644 index d3611091..00000000 --- a/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/.ipynb_checkpoints/current_mirror_ota_optimization-checkpoint.ipynb +++ /dev/null @@ -1,3279 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7SctWEKbxts9", - "metadata": { - "id": "7SctWEKbxts9" - }, - "source": [ - "# **ISSCC 2025 Code-a-Chip Challenge**\n", - "# **Automated Current Mirror OTA Design and Optimization:
From Specification to Layout**\n", - "## Author: Alec S. Adair - The University of Utah, Salt Lake City, UT\n", - "\n", - "alecadair1@gmail.com, alec.adair@utah.edu, https://github.com/alecadair, https://www.linkedin.com/in/alecadair/\n", - "\n", - "Please feel free to reach out!\n", - "\n", - "**Work Licensed Under GPL 3.0**\n", - "\n", - "Details of GPL 3.0 can be read in the LICENSE file of top level directory\n", - "\n", - "---\n", - "\n", - "Welcome to this **ISSCC 2025 Code-a-Chip Challenge** notebook! This notebook demonstrates a full specification to GDS automated design and optimization flow for an **8-transistor current mirror operational transconductance amplifier (OTA)** using the **C/ID method** coupled with the **ALIGN** analog layout generator. The C/ID methodology is an extension of the gm/ID design methodology.\n", - "The C/ID methodology introduces an approach to provide technology-agnostic analytical circuit optimization with systematic convergence for process and temperature variation (PVT) without the need for simulation or computational iteration. This approach enables high performance, rapid, robust, and optimal design with extremely low computational complexity without expensive compute hardware. When coupled with a layout generator such as ALIGN a fully automated flow from specification to layout can be realized. This notebook showcases a fully automated flow from specification to layout to verification with an 8 transistor current mirror OTA example using the C/ID methodology, ALIGN analog layout generator, and **Open Source Skywater130-A PDK**.\n", - "\n", - "---\n", - "## **Notebook Overview**\n", - "\n", - "This notebook is based on the methods and findings presented in the paper:\n", - "\n", - "**\"Analytical Optimization for Robust and Efficient Analog IC Design Automation\"** - *Currently under review for DAC 2025*\n", - "\n", - "Authored by: *Alec Adair and Armin Tajalli*\n", - "\n", - "The paper is currently under review for DAC 2025 and offers deeper insights into the theoretical and practical aspects of the C/ID methodology. It is highly encouraged to read this manuscript as supplemental material to this notebook.\n", - "\n", - "[PDF Manuscript, Design Scripts, and Results Available Here](https://github.com/alecadair/DAC2025_ROAR) \n", - "\n", - "A PDF manuscript is also available here https://github.com/alecadair/CM-OTA-Synthesis/blob/main/AnalyticalOptimization-OTA.pdf\n", - "\n", - "**All design scripts in this flow are intended to be process and technology agnostic.**\n", - "**The open source Skywater130nm-A process is used for all results.**\n", - "\n", - "C/ID is generally pronounced as \"C over ID\", but the author also encourages the colloquial use of the term \"Inverse ID based design\" to refer to gm/ID, C/ID, and other design metrics and methodologies that rely on the inverse of ID (1/ID) .\n", - "\n", - "**This notebook includes:**\n", - "\n", - "0. **Optimization automation and procedure to minimize current in a current mirror OTA and generate layout**\n", - "1. **OTA Design and Optimization Analysis Using C/ID equations derived from traditional design equations**: Key design specifications and constraints.\n", - "2. **C/ID-Based Optimization Workflow**: Step-by-step design methodology for CM OTA.\n", - "3. **Netlist Generation**: SPICE and ALIGN netlists for ideal spice simulation and layout generation.\n", - "4. **Layout Generation and Verification**: Layout generation with ALIGN and DRC, LVS, and netlist extraction using FOSS tools.\n", - "5. **Simulation and Analysis**: Performance evaluation for ideal and post layout simulations.\n", - "\n", - "---\n", - "\n", - "## **Requirements**\n", - "\n", - "1. **Skywater130-A PDK** available at https://github.com/RTimothyEdwards/open_pdks.git
\n", - " **This notebook and corresponding software relies on the pdk being installed at PDK_ROOT Environmental Variable**
\n", - " This is the same convetion and in line with other open source tools such as xschem when designing with skywater130.\n", - "2. **ALIGN Analog Layout Generator** with schematic2layout.py in PATH environment variable - this notebook can also install, but takes a while.\n", - "3. **ngspice** in PATH environment variable\n", - "4. **magic** in PATH environment variable\n", - "5. python3\n", - "---\n", - "\n", - "## **Current Mirror OTA Topology**\n", - "\n", - "\"OTA\n", - "\n", - "---\n", - "\n", - "This notebook is based on the methods and findings presented in the paper:\n", - "\n", - "**\"Analytical Optimization for Robust and Efficient Analog IC Design Automation\"** - *Currently under review for DAC 2025*\n", - "\n", - "Authored by: *Alec Adair and Armin Tajalli*\n", - "\n", - "The paper is currently under review for DAC 2025 and offers deeper insights into the theoretical and practical aspects of the C/ID methodology. It is highly encouraged to read this manuscript as supplemental material to this notebook.\n", - "\n", - "[PDF Manuscript, Design Scripts, and Results Available Here](https://github.com/alecadair/DAC2025_ROAR) \n", - "\n", - "\n", - "**All design scripts in this flow are intended to be process and technology agnostic.**\n", - "**The open source Skywater130nm-A process is used for all results.**\n", - "\n", - "C/ID is generally pronounced as \"C over ID\", but the author also encourages the colloquial use of the term \"Inverse ID based design\" to refer to gm/ID, C/ID, and other design metrics and methodologies that rely on the inverse of ID (1/ID) .\n", - "\n", - "---\n", - "\n", - "## **General Flow**\n", - "\n", - "

\n", - " \"Flow\n", - "

\n", - "\n", - "This notebook provides specification to layout functionality for end-to-end design and verification, including:\n", - "\n", - "0. **LUT Generation**: C/ID is a Lookup Table (LUT) based flow\n", - "1. **Analytical Design and Optimization Analysis**: Analytical optimization can be done with many topologys using C/ID\n", - "2. **SPICE Netlist Generation**: Generates a SPICE netlist for simulation using `ngspice`.\n", - "2. **Layout Generation with Skywater 130nm A Process and ALIGN**: The design is based on the Skywater 130nm A process.\n", - "3. **Simulation and Verification**: Ensure functionality \n", - "\n", - "This notebook follows these steps in order and as outlined in the flow chart diagram above.\n", - "\n", - "---\n", - "\n", - "Feel free to explore, modify, and adjust the specifications and code to adapt it to your specific requirements.\n", - "\n", - "I hope to be a part of the growing movement in the democritization of silicon and open source design while continuing to innovate and optimize the analog IC design flow with you all in this vibrant and passionate community. I truly believe the work this community has done is important for political, technological, academic, and industrial reasons and will benefit humanity in much of the same way Linux and other open source projects have. I hope this is an opportunity for myself to collaborate further with you all and build something special, meaningful, and impactful. I thank you all for your work, passions, inspiration, and contributions to the growing eco-system that this community of IC designers has built.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "e7d3462c-a7a7-41a8-81c2-2e24486e946a", - "metadata": {}, - "source": [ - "## **Tool Installation**\n", - "\n", - "Install needed python libraries to run the flow" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1956ffb0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pip in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (21.3.1)\r\n" - ] - } - ], - "source": [ - "# Update pip \n", - "!pip install --upgrade pip" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c80512df-6cee-483b-9d98-4c70441b7c95", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: matplotlib in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (3.3.4)\n", - 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"Requirement already satisfied: attrs>=17.4.0 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert>=6.0->jupyter-contrib-nbextensions) (22.2.0)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: cffi>=1.0.1 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=6.0->jupyter-contrib-nbextensions) (1.15.1)\n", - "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from packaging->bleach->nbconvert>=6.0->jupyter-contrib-nbextensions) (3.1.4)\n", - "Requirement already satisfied: pycparser in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=6.0->jupyter-contrib-nbextensions) (2.21)\n", - "Requirement already satisfied: parso<0.8.0,>=0.7.0 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from jedi<=0.17.2,>=0.10->ipython>=5.0.0->ipykernel->notebook>=6.0->jupyter-contrib-nbextensions) (0.7.1)\n", - "Requirement already satisfied: wcwidth in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=5.0.0->ipykernel->notebook>=6.0->jupyter-contrib-nbextensions) (0.2.13)\n", - "Requirement already satisfied: immutables>=0.9 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from contextvars->anyio<4,>=3.1.0->jupyter-server->jupyter_nbextensions_configurator>=0.4.0->jupyter-contrib-nbextensions) (0.19)\n", - "Requirement already satisfied: zipp>=0.5 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from importlib-metadata->jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert>=6.0->jupyter-contrib-nbextensions) (3.6.0)\n", - "Requirement already satisfied: pillow in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (8.4.0)\n" - ] - } - ], - "source": [ - "# Install needed python libraries\n", - "!pip install matplotlib\n", - "!pip install pandas\n", - "!pip install jupyterlab-latex\n", - "!pip install jupyter-contrib-nbextensions\n", - "!pip install pillow\n", - "#!pip install gdstk\n", - "#!pip install cairosvg\n", - "#!pip install svglib\n", - "#!pip install cffi==1.15.1\n", - "#!pip install --upgrade cffi==1.15.1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "629b341d-6f17-4ced-b20c-5a4c9d9de6b3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: colorlog in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (6.9.0)\n", - "\u001b[31mERROR: Could not find a version that satisfies the requirement pydantic==1.10.18 (from versions: 0.0.1, 0.0.2, 0.0.3, 0.0.4, 0.0.5, 0.0.6, 0.0.7, 0.0.8, 0.1, 0.2, 0.2.1, 0.3, 0.4, 0.5, 0.6, 0.6.1, 0.6.2, 0.6.3, 0.6.4, 0.7, 0.7.1, 0.8, 0.9, 0.9.1, 0.10, 0.11, 0.11.1, 0.11.2, 0.12, 0.12.1, 0.13, 0.13.1, 0.14, 0.15, 0.16, 0.16.1, 0.17, 0.18, 0.18.1, 0.18.2, 0.19, 0.20a1, 0.20, 0.20.1, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27a1, 0.27, 0.28, 0.29, 0.30, 0.30.1, 0.31, 0.31.1, 0.32, 0.32.1, 0.32.2, 1.0b1, 1.0b2, 1.0, 1.1, 1.1.1, 1.2, 1.3, 1.4, 1.5, 1.5.1, 1.6, 1.6.1, 1.6.2, 1.7, 1.7.1, 1.7.2, 1.7.3, 1.7.4, 1.8, 1.8.1, 1.8.2, 1.9.0a1, 1.9.0a2, 1.9.0, 1.9.1, 1.9.2)\u001b[0m\n", - "\u001b[31mERROR: No matching distribution found for pydantic==1.10.18\u001b[0m\n", - "Requirement already satisfied: z3 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (0.2.0)\n", - "Requirement already satisfied: boto in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from z3) (2.49.0)\n", - "Requirement already satisfied: networkx in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (2.5.1)\n", - "Requirement already satisfied: decorator<5,>=4.3 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from networkx) (4.4.2)\n", - "Requirement already satisfied: flatdict in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (4.0.1)\n", - "Requirement already satisfied: python-gdsii in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (0.2.1)\n", - "Collecting gdspy\n", - " Using cached gdspy-1.6.13.zip (157 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: numpy in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from gdspy) (1.19.5)\n", - "Using legacy 'setup.py install' for gdspy, since package 'wheel' is not installed.\n", - "Installing collected packages: gdspy\n", - " Running setup.py install for gdspy ... \u001b[?25lerror\n", - "\u001b[31m ERROR: Command errored out with exit status 1:\n", - " command: /pri/ala1/Documents/CAD_Custom_Scripts/venv2/bin/python3 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-a35p8b6w/gdspy_c5ac6b8845e441659cb4ec9e32d196cb/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-a35p8b6w/gdspy_c5ac6b8845e441659cb4ec9e32d196cb/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-k5wg4mlf/install-record.txt --single-version-externally-managed --compile --install-headers /pri/ala1/Documents/CAD_Custom_Scripts/venv2/include/site/python3.6/gdspy\n", - " cwd: /tmp/pip-install-a35p8b6w/gdspy_c5ac6b8845e441659cb4ec9e32d196cb/\n", - " Complete output (40 lines):\n", - " running install\n", - " running build\n", - " running build_py\n", - " creating build\n", - " creating build/lib.linux-x86_64-3.6\n", - " creating build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/hobby.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/library.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/path.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/viewer.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/curve.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/__init__.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/label.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/operation.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/polygon.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/gdsiiformat.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " creating build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/09.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/03.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/up.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/01.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/00.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/07.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/04.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/05.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/08.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/down.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/outline.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/06.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/02.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " running build_ext\n", - " building 'gdspy.clipper' extension\n", - " creating build/temp.linux-x86_64-3.6\n", - " creating build/temp.linux-x86_64-3.6/gdspy\n", - " gcc -pthread -Wno-unused-result -Wsign-compare -DDYNAMIC_ANNOTATIONS_ENABLED=1 -DNDEBUG -O2 -g -pipe -Wall -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -fexceptions -fstack-protector-strong -grecord-gcc-switches -m64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -D_GNU_SOURCE -fPIC -fwrapv -O2 -g -pipe -Wall -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -fexceptions -fstack-protector-strong -grecord-gcc-switches -m64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -D_GNU_SOURCE -fPIC -fwrapv -O2 -g -pipe -Wall -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -fexceptions -fstack-protector-strong -grecord-gcc-switches -m64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -D_GNU_SOURCE -fPIC -fwrapv -fPIC -I/pri/ala1/Documents/CAD_Custom_Scripts/venv2/include -I/usr/include/python3.6m -c gdspy/clipper.cpp -o build/temp.linux-x86_64-3.6/gdspy/clipper.o\n", - " gdspy/clipper.cpp:43:10: fatal error: Python.h: No such file or directory\n", - " #include \n", - " ^~~~~~~~~~\n", - " compilation terminated.\n", - " error: command 'gcc' failed with exit status 1\n", - " ----------------------------------------\u001b[0m\n", - "\u001b[31mERROR: Command errored out with exit status 1: /pri/ala1/Documents/CAD_Custom_Scripts/venv2/bin/python3 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-a35p8b6w/gdspy_c5ac6b8845e441659cb4ec9e32d196cb/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-a35p8b6w/gdspy_c5ac6b8845e441659cb4ec9e32d196cb/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-k5wg4mlf/install-record.txt --single-version-externally-managed --compile --install-headers /pri/ala1/Documents/CAD_Custom_Scripts/venv2/include/site/python3.6/gdspy Check the logs for full command output.\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: plotly in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (5.18.0)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from plotly) (8.2.2)\n", - "Requirement already satisfied: packaging in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from plotly) (21.3)\n", - "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from packaging->plotly) (3.1.4)\n" - ] - } - ], - "source": [ - "# Needed libraries for ALIGN\n", - "!pip install colorlog\n", - "!pip install pydantic==1.10.18\n", - "!pip install z3\n", - "!pip install networkx\n", - "!pip install flatdict\n", - "!pip install python-gdsii\n", - "!pip install gdspy\n", - "!pip install plotly" - ] - }, - { - "cell_type": "markdown", - "id": "38a35b3b", - "metadata": {}, - "source": [ - "## **Clone Current Mirror OTA Synthesis Repository**\n", - "\n", - "This Repository contains scripts, templates, and characterizations to run the flow in this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c6aff259", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cloning into 'CM-OTA-Synthesis'...\n", - "remote: Enumerating objects: 723, done.\u001b[K\n", - "remote: Counting objects: 100% (723/723), done.\u001b[K\n", - "remote: Compressing objects: 100% (290/290), done.\u001b[K\n", - "remote: Total 723 (delta 430), reused 717 (delta 427), pack-reused 0 (from 0)\u001b[K\n", - "Receiving objects: 100% (723/723), 12.72 MiB | 27.90 MiB/s, done.\n", - "Resolving deltas: 100% (430/430), done.\n", - "Checking out files: 100% (497/497), done.\n" - ] - } - ], - "source": [ - "!rm -rf CM-OTA-Synthesis\n", - "!git clone https://github.com/alecadair/CM-OTA-Synthesis" - ] - }, - { - "cell_type": "markdown", - "id": "85b7ddd8-e5b6-4122-bc18-976ba2ed3aec", - "metadata": {}, - "source": [ - "## **Import Python Libraries**" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "583e3a55-19f4-4961-83ed-112f985b14b9", - "metadata": {}, - "outputs": [], - "source": [ - "# Import basic libraries and matplotlib graphing libraries\n", - "import matplotlib\n", - "#matplotlib.use('Agg')\n", - "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "import sys, os, getpass, shutil, operator, collections, copy, re, math\n", - "import matplotlib.ticker as mticker\n", - "from matplotlib.ticker import LogLocator\n", - "from matplotlib.colors import LogNorm\n", - "from matplotlib.font_manager import FontProperties\n", - "\n", - "plt.rcParams['svg.fonttype'] = 'none'\n", - "\n", - "# Import gds viewer libraries\n", - "#import gdstk\n", - "#from wand.image import Image\n", - "#import cairosvg\n", - "#from svglib.svglib import svg2rlg\n", - "#from reportlab.graphics import renderPM\n", - "from IPython.display import Image\n", - "\n", - "\n", - "# Set global variables for software\n", - "ROAR_HOME = \"CM-OTA-Synthesis\"\n", - "ROAR_LIB = ROAR_HOME + \"/lib\"\n", - "ROAR_SRC = ROAR_HOME + \"/src\"\n", - "ROAR_CHARACTERIZATION = ROAR_HOME + \"/characterization\"\n", - "ROAR_DESIGN = ROAR_HOME + \"/design\"\n", - "sys.path.append(ROAR_SRC)\n", - "from cid import *\n" - ] - }, - { - "cell_type": "markdown", - "id": "97bae479-4b14-47f5-b560-b7d8c7d60a20", - "metadata": {}, - "source": [ - "## **Install PDK (if needed)**\n", - "\n", - "If $PDK_ROOT is not set then clone the Skywater130 PDK and build it" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "523e5032-1a8e-4c0e-9cac-49c377129c12", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PDK_ROOT is set to: /opt/pdk/open_pdks/sky130\n" - ] - } - ], - "source": [ - "# Check if PDK_ROOT environment variable is set\n", - "# If PDK_ROOT is not set Skywater130 PDK is installed and PDK_ROOT gets set\n", - "if 'PDK_ROOT' in os.environ:\n", - " print(f\"PDK_ROOT is set to: {os.environ['PDK_ROOT']}\")\n", - "else:\n", - " !git clone https://github.com/RTimothyEdwards/open_pdks\n", - " print(\"PDK_ROOT environment variable is not set.\")\n", - " os.system(\"cd ./open_pdks\")\n", - " os.system(\"./configure --enable-sky130-pdk\")\n", - " os.system(\"make\")\n", - " os.system(\"cd ./sky130\")\n", - " pdk_dir = os.getcwd()\n", - " os.environ['PDK_ROOT'] = pdk_dir\n", - " os.system(\"cd ../../\")\n", - " print(\"PDK_ROOT environment variable is not set.\")" - ] - }, - { - "cell_type": "markdown", - "id": "ba7ca37d-77d0-4059-aa1a-c4edfd6119d7", - "metadata": {}, - "source": [ - "## **Install MAGIC (if needed)**\n", - "\n", - "If magic is not in $PATH then install and build magic" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "5f9c8190-74a2-45ae-a6e3-fd95a0b868f7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "magic is available in $PATH.\n" - ] - } - ], - "source": [ - "#\n", - "# If ALIGN is already installed on your system this cell may be skipped.\n", - "# schematic2layout.py must be in your $PATH environmental variable\n", - "#executable_name = \"make\"\n", - "\n", - "# Check if the executable exists in $PATH\n", - "if shutil.which(\"magic\"):\n", - " print(\"magic is available in $PATH.\")\n", - "else:\n", - " # Install magic Layout Editor\n", - " !git clone https://github.com/RTimothyEdwards/magic.git\n", - " os.system(\"cd magic\")\n", - " os.system(\"./configure\")\n", - " os.system(\"make\")\n", - " os.system(\"make install\")\n", - " os.system(\"..\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "776c8a95-f236-4cf2-a747-fe192349f03b", - "metadata": {}, - "source": [ - "## **Install ALIGN (if needed)**\n", - "\n", - "If schematic2layout.py is not in $PATH then install and build ALIGN" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "fed1ad4b-0029-41ff-8c9a-2db8f5002cc6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "schematic2layout.py is available in $PATH.\n" - ] - } - ], - "source": [ - "#\n", - "# If ALIGN is already installed on your system this cell may be skipped.\n", - "# schematic2layout.py must be in your $PATH environmental variable\n", - "#executable_name = \"make\"\n", - "\n", - "# Check if the executable exists in $PATH\n", - "if shutil.which(\"schematic2layout.py\"):\n", - " print(\"schematic2layout.py is available in $PATH.\")\n", - "else:\n", - " # Install ALIGN Layout Generator - This installation takes quite a long time to compile with about 322 modules being built and compiled.\n", - " # This installation takes about 1GB of storage space. Please be patient while it runs.\n", - " # The installation process also has very long output logging.\n", - " !rm -rf ALIGN-public\n", - " !git clone https://github.com/ALIGN-analoglayout/ALIGN-public\n", - " !pip install -v ./ALIGN-public\n" - ] - }, - { - "cell_type": "markdown", - "id": "4164b4b2-2fc5-4b87-818e-9f87b4fb983f", - "metadata": {}, - "source": [ - "## **Lookup Table Generation**\n", - "\n", - "This step is optional in the flow. Lookup tables for the Skywater130A process come pre-packaged in the characterization/sky130 directory.\n", - "Running the following cell will recreate the lookup tables in this directory and results may be different than what is tested.\n", - "\n", - "**When initially running this notebook is recommended to not recreate lookup tables.**\n", - "**By default the function call at the bottom of this cell \"create_lookup_tables(\"SKY130\")\" to generate the lookup tables is commented out. This can be uncommented to run lookup table generation.**\n", - "\n", - "---\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c284ce3c-fde5-45f8-9b7a-e08f683ecdee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lookup Tables Generated\n", - "\u001b[38;5;33m./CM-OTA-Synthesis/characterization/sky130/LUTs_SKY130\u001b[0m\n", - "├── \u001b[38;5;33mn_01v8\u001b[0m\n", - "│   ├── \u001b[38;5;33mLUT_N_1000\u001b[0m\n", - "│   │   ├── nfetff-25.csv\n", - "│   │   ├── nfetff27.csv\n", - "│   │   ├── nfetff75.csv\n", - "│   │   ├── nfetss-25.csv\n", - "│   │   ├── nfetss27.csv\n", - "│   │   ├── nfetss75.csv\n", - "│   │   ├── nfettt-25.csv\n", - "│   │   ├── nfettt27.csv\n", - "│   │   └── nfettt75.csv\n", - "│   ├── \u001b[38;5;33mLUT_N_150\u001b[0m\n", - "│   │   ├── nfetff-25.csv\n", - "│   │   ├── nfetff27.csv\n", - "│   │   ├── nfetff75.csv\n", - "│   │   ├── nfetss-25.csv\n", - "│   │   ├── nfetss27.csv\n", - "│   │   ├── nfetss75.csv\n", - "│   │   ├── nfettt-25.csv\n", - "│   │   ├── nfettt27.csv\n", - "│   │   └── nfettt75.csv\n", - "│   ├── \u001b[38;5;33mLUT_N_200\u001b[0m\n", - "│   │   ├── nfetff-25.csv\n", - "│   │   ├── nfetff27.csv\n", - "│   │   ├── nfetff75.csv\n", - "│   │   ├── nfetss-25.csv\n", - "│   │   ├── nfetss27.csv\n", - "│   │   ├── nfetss75.csv\n", - "│   │   ├── nfettt-25.csv\n", - "│   │   ├── nfettt27.csv\n", - "│   │   └── nfettt75.csv\n", - "│   ├── \u001b[38;5;33mLUT_N_250\u001b[0m\n", - "│   │   ├── nfetff-25.csv\n", - "│   │   ├── nfetff27.csv\n", - "│   │   ├── nfetff75.csv\n", - "│   │   ├── nfetss-25.csv\n", - "│   │   ├── nfetss27.csv\n", - "│   │   ├── nfetss75.csv\n", - "│   │   ├── nfettt-25.csv\n", - "│   │   ├── nfettt27.csv\n", - "│   │   └── nfettt75.csv\n", - "│   ├── \u001b[38;5;33mLUT_N_300\u001b[0m\n", - "│   │   ├── nfetff-25.csv\n", - "│   │   ├── nfetff27.csv\n", - "│   │   ├── nfetff75.csv\n", - "│   │   ├── nfetss-25.csv\n", - "│   │   ├── nfetss27.csv\n", - "│   │   ├── nfetss75.csv\n", - "│   │   ├── nfettt-25.csv\n", - "│   │   ├── nfettt27.csv\n", - "│   │   └── nfettt75.csv\n", - "│   └── \u001b[38;5;33mLUT_N_500\u001b[0m\n", - "│   ├── nfetff-25.csv\n", - "│   ├── nfetff25.csv\n", - "│   ├── nfetff75.csv\n", - "│   ├── nfetss-25.csv\n", - "│   ├── nfetss25.csv\n", - "│   ├── nfetss75.csv\n", - "│   ├── nfettt-25.csv\n", - "│   ├── nfettt25.csv\n", - "│   └── nfettt75.csv\n", - "└── \u001b[38;5;33mp_01v8\u001b[0m\n", - " ├── \u001b[38;5;33mLUT_P_1000\u001b[0m\n", - " │   ├── pfetff-25.csv\n", - " │   ├── pfetff27.csv\n", - " │   ├── pfetff75.csv\n", - " │   ├── pfetss-25.csv\n", - " │   ├── pfetss27.csv\n", - " │   ├── pfetss75.csv\n", - " │   ├── pfettt-25.csv\n", - " │   ├── pfettt27.csv\n", - " │   └── pfettt75.csv\n", - " ├── \u001b[38;5;33mLUT_P_150\u001b[0m\n", - " │   ├── pfetff-25.csv\n", - " │   ├── pfetff27.csv\n", - " │   ├── pfetff75.csv\n", - " │   ├── pfetss-25.csv\n", - " │   ├── pfetss27.csv\n", - " │   ├── pfetss75.csv\n", - " │   ├── pfettt-25.csv\n", - " │   ├── pfettt27.csv\n", - " │   └── pfettt75.csv\n", - " ├── \u001b[38;5;33mLUT_P_200\u001b[0m\n", - " │   ├── pfetff-25.csv\n", - " │   ├── pfetff27.csv\n", - " │   ├── pfetff75.csv\n", - " │   ├── pfetss-25.csv\n", - " │   ├── pfetss27.csv\n", - " │   ├── pfetss75.csv\n", - " │   ├── pfettt-25.csv\n", - " │   ├── pfettt27.csv\n", - " │   └── pfettt75.csv\n", - " ├── \u001b[38;5;33mLUT_P_250\u001b[0m\n", - " │   ├── pfetff-25.csv\n", - " │   ├── pfetff27.csv\n", - " │   ├── pfetff75.csv\n", - " │   ├── pfetss-25.csv\n", - " │   ├── pfetss27.csv\n", - " │   ├── pfetss75.csv\n", - " │   ├── pfettt-25.csv\n", - " │   ├── pfettt27.csv\n", - " │   └── pfettt75.csv\n", - " ├── \u001b[38;5;33mLUT_P_300\u001b[0m\n", - " │   ├── pfetff-25.csv\n", - " │   ├── pfetff27.csv\n", - " │   ├── pfetff75.csv\n", - " │   ├── pfetss-25.csv\n", - " │   ├── pfetss27.csv\n", - " │   ├── pfetss75.csv\n", - " │   ├── pfettt-25.csv\n", - " │   ├── pfettt27.csv\n", - " │   └── pfettt75.csv\n", - " └── \u001b[38;5;33mLUT_P_500\u001b[0m\n", - " ├── pfetff-25.csv\n", - " ├── pfetff25.csv\n", - " ├── pfetff75.csv\n", - " ├── pfetss-25.csv\n", - " ├── pfetss25.csv\n", - " ├── pfetss75.csv\n", - " ├── pfettt-25.csv\n", - " ├── pfettt25.csv\n", - " └── pfettt75.csv\n", - "\n", - "14 directories, 108 files\n" - ] - } - ], - "source": [ - "def create_netlist_from_template(netlist_template, length, corner, temperature):\n", - " if os.path.exists(netlist_template):\n", - " with open(netlist_template, 'r') as net_temp:\n", - " netlist_data = net_temp.read()\n", - " length_str = str(length)\n", - " netlist_data = netlist_data.replace(\"_LENGTH\", length_str)\n", - " netlist_data = netlist_data.replace(\"_CORNER\", corner)\n", - " netlist_data = netlist_data.replace(\"_TEMPERATURE\", temperature)\n", - " return netlist_data\n", - "\n", - "# Netlist parsing line.\n", - "def fix_data_line(text):\n", - " # Remove leading spaces from each line\n", - " text = re.sub(r'^\\s+', '', text, flags=re.MULTILINE)\n", - "\n", - " # Replace one or more spaces with a single comma, but keep newline characters\n", - " text = re.sub(r'[ \\t]+', ',', text)\n", - "\n", - " return text\n", - "\n", - "#\n", - "# Top level function call for lookup table creation\n", - "# This function creates a directory with a hierarchy of lookup tables.\n", - "# The hierarchy model->length->lookup csv file.\n", - "# The following tree shows the example of the file structure created\n", - "#\n", - "# LUTs_SKY130/\n", - "# ├── n_01v8\n", - "# │   ├── LUT_N_1000\n", - "# │   │   ├── nfetff-25.csv\n", - "# │   │   ├── nfetff27.csv\n", - "# │   │   ├── nfetff75.csv\n", - "# │   │   ├── nfetss-25.csv\n", - "# │   │   ├── nfetss27.csv\n", - "# │   │   ├── nfetss75.csv\n", - "# │   │   ├── nfettt-25.csv\n", - "# │   │   ├── nfettt27.csv\n", - "# │   │   └── nfettt75.csv\n", - "# │   ├── LUT_N_150\n", - "# │   │   ├── nfetff-25.csv\n", - "# │   │   ├── nfetff27.csv\n", - "# │   │   ├── nfetff75.csv\n", - "#\n", - "def create_lookup_tables(tech_name=\"\"):\n", - " pdk = \"sky130\"\n", - " luts_dir = \"LUTs_\" + tech_name\n", - " netlists_dir = \"netlists_\" + tech_name\n", - " if os.path.exists(luts_dir):\n", - " shutil.rmtree(luts_dir)\n", - " os.system(\"mkdir \" + luts_dir)\n", - "\n", - " # List of skywater devices to be characterized\n", - " # Default is Regular-threshold 1.8 V devices\n", - " models = [\"01v8\"]\n", - "\n", - " nfet = \"nfet\"\n", - " pfet = \"pfet\"\n", - "\n", - " # Define temperatures and corners to be characterized\n", - " # Defaults is slow slow, typical typical, and fast fast corners\n", - " # With Temperatures of -25, 25, and 75 degrees Celsius\n", - " ss = \"ss\"\n", - " tt = \"tt\"\n", - " ff = \"ff\"\n", - "\n", - " corners = [ss, tt, ff]\n", - "\n", - " cold = \"-25\"\n", - " room = \"25\"\n", - " hot = \"75\"\n", - "\n", - " temperatures = [cold, room, hot]\n", - "\n", - " nsscold = nfet + ss + cold\n", - " nttcold = nfet + tt + cold\n", - " nffcold = nfet + ff + cold\n", - " nssroom = nfet + ss + room\n", - " nttroom = nfet + tt + room\n", - " nffroom = nfet + ff + room\n", - " nsshot = nfet + ss + hot\n", - " ntthot = nfet + tt + hot\n", - " nffhot = nfet + ff + hot\n", - "\n", - " psscold = pfet + ss + cold\n", - " pttcold = pfet + tt + cold\n", - " pffcold = pfet + ff + cold\n", - " pssroom = pfet + ss + room\n", - " pttroom = pfet + tt + room\n", - " pffroom = pfet + ff + room\n", - " psshot = pfet + ss + hot\n", - " ptthot = pfet + tt + hot\n", - " pffhot = pfet + ff + hot\n", - "\n", - " # List of lengths to be characterized in micrometers\n", - " # This list can be edited for lengths of interest\n", - " lengths = [\".150\", \".200\", \".250\", \".300\", \".500\", \"1.000\"]\n", - "\n", - "\n", - " # List of corners to be characterized\n", - " ncorners = [nsscold, nttcold, nffcold,\n", - " nssroom, nttroom, nffroom,\n", - " nsshot, ntthot, nffhot]\n", - "\n", - " pcorners = [psscold, pttcold, pffcold,\n", - " pssroom, pttroom, pffroom,\n", - " psshot, ptthot, pffhot]\n", - "\n", - "\n", - " run_n = True\n", - " run_p = True\n", - "\n", - " netlists_dir = \"netlists\"\n", - " netlist_mkdir = \"mkdir \" + netlists_dir\n", - " if not os.path.exists(netlists_dir):\n", - " os.system(netlist_mkdir)\n", - "\n", - " netlist_template_file = \"characterization/char_template.cir\"\n", - " for model in models:\n", - " n_model = \"n_\" + model\n", - " p_model = \"p_\" + model\n", - " n_model_dir = luts_dir + \"/\" + n_model\n", - " p_model_dir = luts_dir + \"/\" + p_model\n", - " if run_n == True:\n", - " if os.path.exists(n_model_dir):\n", - " shutil.rmtree(n_model_dir)\n", - " os.system(\"mkdir \" + n_model_dir)\n", - " if run_p == True:\n", - " if os.path.exists(p_model_dir):\n", - " shutil.rmtree(p_model_dir)\n", - " os.system(\"mkdir \" + p_model_dir)\n", - " for length in lengths:\n", - " length_str = str(length.replace(\".\", \"\"))\n", - " n_length_dir = n_model_dir + \"/LUT_N_\" + str(length_str)\n", - " p_length_dir = p_model_dir + \"/LUT_P_\" + str(length_str)\n", - " if run_n == True:\n", - " if os.path.exists(n_length_dir):\n", - " shutil.rmtree(n_length_dir)\n", - " os.system(\"mkdir \" + n_length_dir)\n", - " if run_p == True:\n", - " if os.path.exists(p_length_dir):\n", - " shutil.rmtree(p_length_dir)\n", - " os.system(\"mkdir \" + p_length_dir)\n", - " for corner in corners:\n", - " for temp in temperatures:\n", - " print(\"Creating netlist...\")\n", - " edited_netlist = create_netlist_from_template(netlist_template=netlist_template_file, length=length, corner=corner, temperature=temp)\n", - " netlist_name = model + \"_\" + length + \"_\" + corner + \"_\" + temp + \".cir\"\n", - " corner_name = corner+temp\n", - " with open(netlist_name, 'w') as file:\n", - " file.write(edited_netlist)\n", - " print(\"Running characterization...\")\n", - " os.system(\"ngspice -b -n \" + netlist_name)\n", - " if os.path.exists(n_length_dir) and os.path.exists(\"nfet_cid_characterization.csv\"):\n", - " with open(\"nfet_cid_characterization.csv\", 'r') as file:\n", - " lines = file.readlines()\n", - " with open(\"nfet_cid_characterization.csv\", 'w') as file:\n", - " for i, line in enumerate(lines):\n", - " line = fix_data_line(line)\n", - " if i == 0:\n", - " #file.write(line.rstrip('\\n') + \",W,L,pdk\\n\")\n", - " file.write(line)\n", - " else:\n", - " #file.write(line.rstrip('\\n') + \"0.42,\" + str(length) + \",\" + pdk + \"\\n\")\n", - " file.write(line.rstrip('\\n') + \"0.840,\" + str(length) + \",\" + pdk + \"\\n\")\n", - " with open(\"pfet_cid_characterization.csv\", 'r') as file:\n", - " lines = file.readlines()\n", - " with open(\"pfet_cid_characterization.csv\", 'w') as file:\n", - " for i, line in enumerate(lines):\n", - " line = fix_data_line(line)\n", - " if i == 0:\n", - " file.write(line)\n", - " #file.write(line.rstrip('\\n') + \",W,L,pdk,\\n\")\n", - " else:\n", - " #file.write(line.rstrip('\\n') + \"0.42,\" + str(length) + \",\" + pdk + \"\\n\")\n", - " file.write(line.rstrip('\\n') + \"0.840,\" + str(length) + \",\" + pdk + \"\\n\")\n", - " os.system(\"mv nfet_cid_characterization.csv \" + n_length_dir + \"/nfet\" + corner_name + \".csv\")\n", - " if os.path.exists(n_length_dir) and os.path.exists(\"pfet_cid_characterization.csv\"):\n", - " os.system(\"mv pfet_cid_characterization.csv \" + p_length_dir + \"/pfet\" + corner_name + \".csv\")\n", - " os.system(\"mv \" + netlist_name + \" netlists\")\n", - " os.system(\"mv \" + luts_dir + \"CM-OTA-Synthesis/characterization/sky130\")\n", - " \n", - "# Uncomment the following line of code to generate lookup tables \n", - "#create_lookup_tables(\"SKY130\")\n", - "print(\"Lookup Tables Generated\")\n", - "!tree ./CM-OTA-Synthesis/characterization/sky130/LUTs_SKY130" - ] - }, - { - "cell_type": "markdown", - "id": "559782af-92dd-43c2-955a-3706fe16ec9f", - "metadata": {}, - "source": [ - "## **Storing The Lookup Tables and Performing Lookups**\n", - "\n", - "This notebook provides custom code and objects for storing lookup tables and performing lookups. This code is in the src/cid.py file. The main object for storing LUTs and performing lookups is called a CIDCorner. The CIDCorner object represents one corner of characterization lookup table from a csv file generated in the previous cell. The CIDCorner stores a lookup table as a pandas data frame from a lookup csv file. The CIDCorner contains functionality for performing lookups with interprebility, meaning an arbitrary value can be looked up and interpololation of data is done on for the lookup.\n", - "\n", - "A CIDCollection object contains a set of CIDCorner objects and also has lookup functionality. When doing a lookup from a CIDCollection object a vector of lookup values is returned instead of a scalar. Each value in the vector represents a respective lookup from a CIDCorner.\n", - "\n", - "---\n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "93e35c0c-c67e-41a7-8729-120a07a1e172", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lookup Tables Stored in ./CM-OTA-Synthesis/characterization\n", - "Lookup Table CID Objects created\n" - ] - } - ], - "source": [ - "# Variable for base directory of LUTs\n", - "lut_dir = ROAR_CHARACTERIZATION\n", - "\n", - "print(\"Lookup Tables Stored in \" + lut_dir)\n", - "# The CIDCorner object provides functionality for storing lookup tables from csv characterization files generated in the cell above.\n", - "# This object also provides functionality for interpolated lookups as well as graphing parameters. \n", - "\n", - "# Define CIDCorner objects for -25, 25, and 75 degree temperature characterizations for nfet and pfet devices \n", - "nfet_nominal = CIDCorner(corner_name=\"nfet_500n_nominal\",\n", - " lut_csv=lut_dir + \"/sky130/LUTs_SKY130/n_01v8/LUT_N_500/nfettt25.csv\",\n", - " vdd=1.8)\n", - "\n", - "pfet_nominal = CIDCorner(corner_name=\"pet_500n_nominal\",\n", - " lut_csv=lut_dir + \"/sky130/LUTs_SKY130/p_01v8/LUT_P_500/pfettt25.csv\",\n", - " vdd=1.8)\n", - "\n", - "nfet_cold = CIDCorner(corner_name=\"nfet_500n_nominal\",\n", - " lut_csv=lut_dir + \"/sky130/LUTs_SKY130/n_01v8/LUT_N_500/nfettt-25.csv\",\n", - " vdd=1.8)\n", - "\n", - "pfet_cold = CIDCorner(corner_name=\"pet_500n_nominal\",\n", - " lut_csv=lut_dir + \"/sky130/LUTs_SKY130/p_01v8/LUT_P_500/pfettt-25.csv\",\n", - " vdd=1.8)\n", - "\n", - "nfet_hot = CIDCorner(corner_name=\"nfet_500n_nominal\",\n", - " lut_csv=lut_dir + \"/sky130/LUTs_SKY130/n_01v8/LUT_N_500/nfettt75.csv\",\n", - " vdd=1.8)\n", - "\n", - "pfet_hot = CIDCorner(corner_name=\"pfet_500n_nominal\",\n", - " lut_csv=lut_dir + \"/sky130/LUTs_SKY130/p_01v8/LUT_P_500/pfettt75.csv\",\n", - " vdd=1.8)\n", - "\n", - "# A CIDCollection object is a collection of CIDCorners with lookup functionality \n", - "# returning a vector instead of a scalar. When doing a lookup using a CIDDevice object \n", - "# a vector of values is returned with each value representing one corner's lookup. The CIDDevice\n", - "\n", - "#nfet_device = CIDCornerCollection(device_name=\"nfet_150n\", vdd=1.8,\n", - "# lut_directory=lut_dir + \"/sky130/LUTs_SKY130/n_01v8/LUT_N_500\",\n", - "# corner_list=None)\n", - "#pfet_device = CIDCornerCollection(device_name=\"pfet_150n\", vdd=1.8,\n", - "# lut_directory=lut_dir + \"/sky130/LUTs_SKY130/p_01v8/LUT_P_500\",\n", - "# corner_list=None)\n", - "\n", - "print(\"Lookup Table CID Objects created\")" - ] - }, - { - "cell_type": "markdown", - "id": "94840207-119c-4394-a1ca-e28943329e18", - "metadata": {}, - "source": [ - "## **Analytical OTA Analysis and Objective Function Derivation**\n", - "---\n", - "
\n", - "
\n", - " \"Analysis\n", - "
\n", - "
\n", - " \"Analysis\n", - "
\n", - "
\n", - " \"Analysis\n", - "
\n", - "
\n", - " \"Analysis\n", - "
\n", - "
\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "e824f389-1efe-4e23-bc10-78e1c66d6cec", - "metadata": {}, - "source": [ - "## **Python Function Definition for Objective Function**\n", - "\n", - "The following function defines the objective function in Equation 25 from the analytical analysis in the cell above. This function takes in a two CIDCorner objects for N and P value lookups as well as values for gm/ID for the N and P devices to be evaluated at. In addition to this it takes alpha as a parameter. Alpha defines the stability criterion as shown in Equation 14 in the analysis. \n", - "\n", - "This function also takes a gain-bandwidth product specification gbw in Hz, capactive loading specification cload in Farads, gain specification in volts/volt, and an optional thermal noise specification.\n", - "\n", - "In all codes, parameters that are divided by ID are denoted with prefix k. I.E. gm/ID = kgm, cgs/ID = kcgs \n" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "e8b126c3-b502-48c3-8fde-4150bac3fe33", - "metadata": {}, - "outputs": [], - "source": [ - "#\n", - "# The following function represents the objective function to be minimized.\n", - "# This function calculates the total current of the OTA.\n", - "# In the analysis above, Equation 25 represents the standard form objective function to be minimized and optimized on.\n", - "#\n", - "def total_current_ota(ncorner, pcorner, kgm_n, kgm_p, alpha, gbw, cload, gain_spec, thermal_noise_spec=0):\n", - " \n", - " # Set physical constants and define f1 - unity gain frequency\n", - " f1 = 2*math.pi*gbw\n", - " k = 1.380649e-23\n", - " T = 300.15\n", - " gamma = 8/3\n", - " kgm1 = kgm_n\n", - " kgm2 = kgm_n\n", - " kgm5 = kgm_n\n", - " kgm6 = kgm_n\n", - " kgm3 = kgm_p\n", - " kgm4 = kgm_p\n", - " kgm7 = kgm_p\n", - " kgm8 = kgm_p\n", - " \n", - " # Perform lookups for design invariants (gm/id, c/id)\n", - " kcgs_1 = ncorner.lookup(param1=\"kgm\", param2=\"kcgs\", param1_val=kgm1)\n", - " kcds_1 = ncorner.lookup(param1=\"kgm\", param2=\"kcds\", param1_val=kgm1)\n", - " kcgs_8 = pcorner.lookup(param1=\"kgm\", param2=\"kcgs\", param1_val=kgm8)\n", - " kcgd_8 = pcorner.lookup(param1=\"kgm\", param2=\"kcgd\", param1_val=kgm8)\n", - " kcds_8 = pcorner.lookup(param1=\"kgm\", param2=\"kcds\", param1_val=kgm8)\n", - " kcgs_6 = ncorner.lookup(param1=\"kgm\", param2=\"kcgs\", param1_val=kgm6)\n", - " kcgd_6 = ncorner.lookup(param1=\"kgm\", param2=\"kcgd\", param1_val=kgm6)\n", - " kcds_6 = ncorner.lookup(param1=\"kgm\", param2=\"kcds\", param1_val=kgm6)\n", - " kcgs_4 = pcorner.lookup(param1=\"kgm\", param2=\"kcgs\", param1_val=kgm4)\n", - " kcds_4 = pcorner.lookup(param1=\"kgm\", param2=\"kcds\", param1_val=kgm4)\n", - " kgds_6 = ncorner.lookup(param1=\"kgm\", param2=\"kgds\", param1_val=kgm6)\n", - " kgds_8 = ncorner.lookup(param1=\"kgm\", param2=\"kgds\", param1_val=kgm8)\n", - "\n", - " # Calculate gain, Kco, Beta, and total current from equations in analysis\n", - " gain = kgm1/(kgds_6 + kgds_8)\n", - " kcout = kcgd_8 + kcds_8 + kcgd_6 + kcds_6\n", - " beta_num = kgm4 - 2*math.pi*alpha*gbw*kcgs_4\n", - " beta_denom = (2*math.pi*alpha*gbw*(kcgs_8 + kcgd_8))\n", - " beta = beta_num/beta_denom\n", - " kcs_8 = kcgs_8 + kcgd_8\n", - " kcs_6 = kcds_6 + kcgs_6\n", - " total_current_num = alpha*cload*f1*f1*(kcs_8 - kcgs_4) + f1*cload*kgm4\n", - " total_current_denom = (kgm4 - alpha*f1*kcgs_4)*(kgm1 - f1*kcout)\n", - " total_current = total_current_num/total_current_denom\n", - " \n", - " # Multiply total current by 2 since this equation represents half-circuit model\n", - " ota_total_current = total_current*2\n", - "\n", - " # Calculate currents and parameters for individual devices\n", - " m1_current = total_current/(1 + beta)\n", - " m8_current = total_current - m1_current\n", - " m6_current = m8_current\n", - " m4_current = m1_current\n", - " m2_cload = (kcgs_1 + kcds_1)*m1_current + kcs_8*m8_current + kcds_4*m1_current\n", - " m4_cload = m2_cload\n", - " m6_cload = ((kcs_6 + kcds_8 + kcgs_8)*m6_current) + cload\n", - " m8_cload = m6_cload\n", - " m1_gm = kgm1*m1_current\n", - " m6_gm = kgm2*m6_current\n", - " m8_gm = m6_gm\n", - " m4_gm = m1_gm\n", - " ft_m8 = m8_gm/(2*math.pi*m8_cload)\n", - " ft_m2 = m1_gm/(2*math.pi*m2_cload)\n", - " ft_m4 = m4_gm/(2*math.pi*m4_cload)\n", - " ft_m6 = m6_gm/(2*math.pi*m6_cload)\n", - "\n", - " # Calculate the thermal noise for the OTA\n", - " thermal_rms_noise = k*T*(ft_m2/(kgm1*m1_current) + ft_m4/(kgm1*m1_current) + ft_m6/(kgm2*m6_current) + ft_m8/(kgm2*m6_current))\n", - "\n", - " # Check if calculation is valid, passes specification, and is in the constrained solution space\n", - " beta_valid = True\n", - " gain_valid = True\n", - " thermal_noise_valid = True\n", - " if beta < 1:\n", - " beta_valid = False\n", - " if gain < gain_spec:\n", - " gain_valid = False\n", - " if thermal_rms_noise < thermal_noise_spec:\n", - " thermal_noise_valid = False\n", - " \n", - " # Return currents, beta, kcout, gain, and validity of evaluation in solution space\n", - " return total_current, m1_current, m6_current, beta, kcout, gain, thermal_rms_noise, beta_valid, gain_valid, thermal_noise_valid, kcout" - ] - }, - { - "cell_type": "markdown", - "id": "ba586402-9194-4665-9cda-c2f0a55b6545", - "metadata": {}, - "source": [ - "---\n", - "## **Visualizing the Solution Space with Specifications and Constraints Applied**\n", - "\n", - "Since the objective function is a 3 dimensional function with two independent variables, it can be visualized using a 3D plot.\n", - "The following code shows the current consumption for the OTA across the gm/ID space for the three different temperature corners defined above. The specification for the OTA is the following\n", - "\n", - "Gain Bandwidth Product (Unity Gain Frequency): 100MHz\n", - "\n", - "Capactive Load: 4pF\n", - "\n", - "Phase Margin: > 60°\n", - "\n", - "Gain: > 50 V/V (~34dB)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "6b6d090d-e41a-4f1b-87e0-f18bec1b78b7", - "metadata": {}, - "outputs": [], - "source": [ - "# Set minimum, bandwidth and gain-bandwidth product, and capacitve loading specification\n", - "av= 50\n", - "bw = 2e6\n", - "gbw = bw * av\n", - "cload = 4e-12\n", - "thermal_noise = 500e-9\n", - "\n", - "# Set Stability Criterion\n", - "tan_thirty = math.tan(30*math.pi/180)\n", - "alpha = 1/tan_thirty\n", - "two_pi_alpha_gbw = 2*math.pi*alpha*gbw\n", - "f2 = alpha*gbw" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "963e106e-a271-42cb-8b3e-716cf55020f0", - "metadata": {}, - "outputs": [], - "source": [ - "# Define function to do plotting for one corner \n", - "def plot_solution_space_cm_ota(lookup_ncorner, lookup_pcorner, alpha, gain, bw, cload, therm_noise, fig, ax1, ax2, ax3, ax4,\n", - " color_map, beta_color, gain_color, alpha_graph, alpha_region, hatch_mark, marker_size, line_style,\n", - " kgm_n_max, kgm_p_max, map_label, edge_color, legend_str, num_samples):\n", - " gbw = gain*bw\n", - " kgm_n_v = lookup_ncorner.df[\"kgm\"]\n", - " kgm_p_v = lookup_pcorner.df[\"kgm\"]\n", - " max_n = max(kgm_n_v)\n", - " max_p = max(kgm_p_v)\n", - " min_n = min(kgm_n_v)\n", - " min_p = min(kgm_p_v)\n", - " kgm_min = min(min_n, min_p)\n", - " kgm_max = max(max_n, max_p)\n", - " current_max = 5000\n", - " current_min = 120\n", - " kgm_min = 0.1\n", - " if kgm_min < 0:\n", - " kgm_min = 0.001\n", - " kgm_min = 0.1\n", - " kgm_max = 26.5\n", - " kgm_vals = np.linspace(kgm_min, kgm_max, num_samples)\n", - " kgm1_grid, kgm2_grid = np.meshgrid(kgm_vals, kgm_vals)\n", - " z = np.zeros_like(kgm1_grid)\n", - " z_log = np.zeros_like(kgm1_grid)\n", - " kcout = np.zeros_like(kgm1_grid)\n", - " kcout_log = np.zeros_like(kgm1_grid)\n", - " gain_v_v = np.zeros_like(kgm1_grid)\n", - " gain_db = np.zeros_like(kgm1_grid)\n", - " beta = np.zeros_like(kgm1_grid)\n", - " beta_valid_grid = np.zeros_like(kgm1_grid)\n", - " gain_valid_grid = np.zeros_like(kgm1_grid)\n", - " kco_max = 1e-05\n", - " if map_label:\n", - " kgm_init_n_max = kgm_n_max\n", - " kgm_init_p_max = kgm_p_max\n", - " for i in range(len(kgm_vals)):\n", - " for j in range(len(kgm_vals)):\n", - " total_current, m1_current_i_j, m6_current_i_j, beta_i_j, kcout_i_j, gain_i_j, thermal_rms_noise_i_j,beta_valid_i_j, gain_valid, thermal_noise_valid, kc_out = total_current_ota(lookup_ncorner, lookup_pcorner, kgm_vals[i], kgm_vals[j],\n", - " alpha, gbw, cload, gain_spec=gain, thermal_noise_spec=therm_noise)\n", - " if kgm_vals[j] > kgm_p_max:\n", - " total_current = -1\n", - " #kcout_i_j = np.nan\n", - " if kgm_vals[i] > kgm_n_max:\n", - " total_current = -1\n", - " #kcout_i_j = np.nan\n", - " total_current = total_current * 1e6\n", - " gain_db_i_j = 20*math.log10(gain_i_j)\n", - " if total_current < current_min:\n", - " total_current = np.nan\n", - " gain_i_j = np.nan\n", - " gain_db_i_j = np.nan\n", - " gain_valid = False\n", - " if total_current > current_max:\n", - " total_current = current_max\n", - " if kcout_i_j > kco_max or kgm_vals[j] > 18.8 or kgm_vals[i] > 26.26:\n", - " kcout_i_j = np.nan\n", - "\n", - " z[i, j] = total_current\n", - " z_log[i, j] = math.log10(total_current)\n", - " kcout[i, j] = kcout_i_j\n", - " kcout_log[i, j] = math.log10(kcout_i_j)\n", - "\n", - " gain_valid_grid[i, j] = gain_valid\n", - " gain_v_v[i, j] = gain_i_j\n", - " gain_db[i, j] = gain_db_i_j\n", - " beta_valid_grid[i, j] = beta_valid_i_j\n", - "\n", - " arial_bold = FontProperties(fname=\"CM-OTA-Synthesis/src/fonts/ArialNarrow/arialnarrow_bold.ttf\")\n", - " font_size = 12\n", - " current_vals = [kgm_vals, z]\n", - " gain_vals = [kgm_vals, gain_v_v]\n", - " kco_vals = [kgm_vals, kcout]\n", - " current_ticks = np.array([100, 250, 500, 1000, 2500, 5000])\n", - " surf_i_total = ax1.plot_surface(kgm1_grid, kgm2_grid, z_log, lw=0.5, cmap=color_map, edgecolor=edge_color, rstride=3, cstride=3, alpha=alpha_graph, label=legend_str)\n", - " if map_label:\n", - " ax1.set_xlabel('gm/ID PFETs [1/V]', font=arial_bold, fontsize=font_size)\n", - " ax1.set_ylabel('gm/ID NFETs [1/V]', font=arial_bold, fontsize=font_size)\n", - " ax1.set_zlabel(\"Total Current [uA]\", font=arial_bold, fontsize=font_size)\n", - " ax1.xaxis._axinfo['grid'].update(color='gray', linestyle='--', linewidth=0.5)\n", - " ax1.yaxis._axinfo['grid'].update(color='gray', linestyle='--', linewidth=0.5)\n", - " ax1.zaxis._axinfo['grid'].update(color='gray', linestyle='--', linewidth=0.5)\n", - " ax1.set_zticks(np.log10(current_ticks))\n", - " ax1.set_zticklabels(current_ticks)\n", - " ax1.set_xlim(kgm_min, kgm_p_max)\n", - " ax1.set_ylim(kgm_min, kgm_n_max)\n", - " ax1.set_zlim(2, 3.9)\n", - "\n", - " surf_kco_total = ax3.plot_surface(kgm1_grid, kgm2_grid, kcout_log, lw=0.5, cmap=color_map, edgecolor=edge_color, rstride=3, cstride=3, alpha=alpha_graph)\n", - " def log_tick_formatter(val, pos=None):\n", - " exponent = int(np.log10(val))\n", - " return f\"$10^{{{exponent}}}$\" # remove int() if you don't use MaxNLocator\n", - "\n", - " ax3.zaxis.set_major_formatter(mticker.FuncFormatter(log_tick_formatter))\n", - " ax3.set_xlabel('gm/ID PFETs [1/V]', font=arial_bold, fontsize=font_size)\n", - " ax3.set_ylabel('gm/ID NFETs [1/V]', font=arial_bold, fontsize=font_size)\n", - " ax3.set_zlabel('Cout/ID6,8 [F/A]', font=arial_bold, fontsize=font_size)\n", - "\n", - " kco_ticks = np.array([1e-11, 1e-9, 1e-7, 1e-05])\n", - " ax3.set_zticks(np.log10(kco_ticks))\n", - " ax3.set_zticklabels(kco_ticks)\n", - " if map_label:\n", - " ax3.set_xlim(kgm_min, kgm_p_max)\n", - " ax3.set_ylim(kgm_min, kgm_n_max)\n", - " ax3.xaxis._axinfo['grid'].update(color='gray', linestyle='--', linewidth=0.5)\n", - " ax3.yaxis._axinfo['grid'].update(color='gray', linestyle='--', linewidth=0.5)\n", - " ax3.zaxis._axinfo['grid'].update(color='gray', linestyle='--', linewidth=0.5)\n", - " surf_gain_total = ax4.plot_surface(kgm1_grid, kgm2_grid, gain_v_v, lw=0.5, cmap=color_map, edgecolor=edge_color, rstride=3, cstride=3, alpha=alpha_graph)\n", - " def log_tick_formatter(val, pos=None):\n", - " return f\"$10^{{{int(val)}}}$\" # remove int() if you don't use MaxNLocator\n", - " ax4.set_xlabel('gm/ID PFETs [1/V]', font=arial_bold, fontsize=font_size)\n", - " ax4.set_ylabel('gm/ID NFETs [1/V]', font=arial_bold, fontsize=font_size)\n", - " ax4.set_zlabel('DC Gain [V/V]', font=arial_bold, fontsize=font_size)\n", - " ax4.xaxis._axinfo['grid'].update(color='gray', linestyle='--', linewidth=0.5)\n", - " ax4.yaxis._axinfo['grid'].update(color='gray', linestyle='--', linewidth=0.5)\n", - " ax4.zaxis._axinfo['grid'].update(color='gray', linestyle='--', linewidth=0.5)\n", - " if map_label:\n", - " ax4.set_xlim(kgm_min, kgm_p_max)\n", - " ax4.set_ylim(kgm_min, kgm_n_max)\n", - " beta_false_mask = beta_valid_grid == False\n", - " gain_false_mask = gain_valid_grid == False\n", - " gain_false_mask = gain_valid_grid == False\n", - " contour2 = ax2.contourf(kgm1_grid, kgm2_grid, z_log, levels=10, alpha=alpha_graph, cmap=color_map)\n", - " ax2.set_xlabel('gm/ID PFETs [1/V]', font=arial_bold, fontsize=font_size)\n", - " ax2.set_ylabel('gm/ID NFETs [1/V]', font=arial_bold, fontsize=font_size)\n", - " # Add gridlines for better readability\n", - " ax2.grid(True, which='both', linestyle='--', linewidth=0.5)\n", - " ax2.grid(True, which='both', linestyle='--', linewidth=0.5)\n", - " cbar4 = fig.colorbar(contour2, ax=ax2, shrink=0.33)\n", - " cbar4.set_ticks(np.log10(current_ticks))\n", - " cbar4.set_ticklabels(current_ticks)\n", - " if map_label:\n", - " ax2.set_ylim(kgm_min, kgm_n_max)\n", - " ax2.set_xlim(kgm_min, kgm_p_max)\n", - " #ax2.invert_xaxis()\n", - " ax2.set_ylim(0.1, 26.26)\n", - " ax2.set_xlim(0.1, 18.8)\n", - " cbar4.set_label(legend_str, font=arial_bold, fontsize=font_size)\n", - " return current_vals, gain_vals, kco_vals, gain_false_mask\n", - "\n", - "# returns true if all num1, num2, and num3 are within difference diff of eachother\n", - "# diff default is 5%\n", - "def number_convergence(num1, num2, num3, diff=0.05):\n", - " #mean = (num1 + num2 + num3)/3\n", - " if num1 == np.nan or num2 == np.nan or num3 == np.nan:\n", - " return False\n", - " def within_diff(a, b):\n", - " ratio = abs(a - b) / max(abs(a), abs(b))\n", - " converge = ratio <= diff\n", - " return converge\n", - " converged = False\n", - " converged1 = within_diff(num1, num2)\n", - " converged2 = within_diff(num2, num3)\n", - " converged3 = within_diff(num1, num3)\n", - " if converged1 and converged2 and converged3:\n", - " converged = True\n", - " return converged" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "5abc0f80-5620-4336-84f4-5ea096950529", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "The current consumption graphs are assuming a half-circuit model.\n", - "This current must be doubled to account for the entire current consumption of the OTA.\n", - "\n" - ] - } - ], - "source": [ - "# Define the number of samples to be taken in the x and y dimension for the objective function.\n", - "# 50 gives a reasonable balance between resolution and evaluation. To visualize the design space\n", - "# num_samples*num_samples need to be taken, this can take a little while to compute (one or two minutes for 70 samples)\n", - "num_samples = 50\n", - "\n", - "# Create plotting objects, set colors, and create arrays for results\n", - "fig = plt.figure(figsize=(14, 10))\n", - "ax1 = fig.add_subplot(221, projection='3d')\n", - "ax2 = fig.add_subplot(222)\n", - "ax3 = fig.add_subplot(223, projection='3d')\n", - "ax4 = fig.add_subplot(224, projection='3d')\n", - "color_map_list = [\"Blues\", \"Greens\", \"Reds\"]\n", - "beta_color_list = [\"b\", \"g\", \"r\"]\n", - "line_style_list = [\"r-\", \"g-\", \"b-.\", \"m-\", \"c-.\", \"r--\", \"b--\", \"g--\", \"m--\"]\n", - "alpha_graph = 0.77\n", - "marker_size = 40\n", - "current_vectors = []\n", - "kgm_vectors = []\n", - "kgm_n = [26.26, 26.26, 26.26]\n", - "kgm_p = [18.8, 16.5, 15.25]\n", - "edge_colors = ['royalblue', 'green', 'red']\n", - "legend_strs = [\"-25°C\", \"25°C\", \"75°C\"]\n", - "map_label = True\n", - "current_arrays = []\n", - "gain_arrays = []\n", - "kco_arrays = []\n", - "kgm_grids = []\n", - "gain_masks = []\n", - "alpha_region = 0.3\n", - "hatches = ['/', '\\ ', '-']\n", - "\n", - "# Plot the solution space using plo_solution_space_cm_ota function or -25°, 25°, and 75° Celsius\n", - "n_list = [nfet_cold, nfet_nominal, nfet_hot]\n", - "p_list = [pfet_cold, pfet_nominal, pfet_hot]\n", - "for i in range(len(n_list)):\n", - " nfet_corner = n_list[i]\n", - " pfet_corner = p_list[i]\n", - " color_map = color_map_list[i]\n", - " beta_color = beta_color_list[0]\n", - " gain_color = beta_color_list[0]\n", - " edge_color = edge_colors[i]\n", - " line_style = line_style_list[i]\n", - " legend_str = legend_strs[i]\n", - " hatch_mark = hatches[i]\n", - " current, gain, kco, gain_mask = plot_solution_space_cm_ota(lookup_ncorner=nfet_corner, lookup_pcorner=pfet_corner, alpha=alpha,gain=av,\n", - " bw=bw, cload=cload, therm_noise=thermal_noise,\n", - " fig=fig, ax1=ax1, ax2=ax2, ax3=ax3, ax4=ax4, color_map=color_map, beta_color=beta_color,\n", - " gain_color=gain_color, alpha_graph=alpha_graph, alpha_region=alpha_region, hatch_mark=hatch_mark,\n", - " marker_size=marker_size, line_style=line_style, kgm_n_max=kgm_n[i], kgm_p_max=kgm_p[i],\n", - " map_label=map_label, edge_color=edge_color, legend_str=legend_str, num_samples=num_samples)\n", - "\n", - " currents = 0\n", - " kgms = 0\n", - " current_vectors.append(currents)\n", - " kgm_vectors.append(kgms)\n", - " map_label = False\n", - " kco_arrays.append(kco[1])\n", - " gain_arrays.append(gain[1])\n", - " current_arrays.append(current[1])\n", - " kgm_grids.append(current[0])\n", - " gain_masks.append(gain_mask)\n", - " marker_size = marker_size - 10\n", - " alpha_region = alpha_region - 0.1\n", - "\n", - "kgm_grid = kgm_grids[0]\n", - "kgm_grid = np.meshgrid(kgm_grid, kgm_grid)\n", - "convergence_grid = np.zeros((len(kgm_grid[0]), len(kgm_grid[0])))\n", - "current0 = current_arrays[0]\n", - "current1 = current_arrays[1]\n", - "current2 = current_arrays[2]\n", - "for i in range(len(kgm_grid[0])):\n", - " for j in range(len(kgm_grid[0])):\n", - " i0 = current0[i, j]\n", - " i1 = current1[i, j]\n", - " i2 = current2[i, j]\n", - " convergence_grid[i, j] = number_convergence(i0, i1, i2, diff=0.10)\n", - "conv_false_mask = convergence_grid == False\n", - "ax2.contourf(kgm_grid[0], kgm_grid[1], gain_masks[0], levels=[0.5, 1], hatches=['/'], alpha=0)\n", - "ax2.contourf(kgm_grid[0], kgm_grid[1], gain_masks[1], levels=[0.5, 1], hatches=['\\ '], alpha=0)\n", - "ax2.contourf(kgm_grid[0], kgm_grid[1], gain_masks[2], levels=[0.5, 1], hatches=['-'], alpha=0)\n", - "ax2.contour(kgm_grid[0], kgm_grid[1], conv_false_mask, levels=[0.5, 1], alpha=1)\n", - "\n", - "ax2.set_ylim(0.1, 26.26)\n", - "ax2.set_xlim(0.1, 18.8)\n", - "\n", - "ax1.view_init(elev=25, azim=195)\n", - "ax3.view_init(elev=25, azim=195)\n", - "ax4.view_init(elev=25, azim=195)\n", - "\n", - "ax1.set_title(\"OTA Total Current Consumption vs. gm/ID Space\")\n", - "ax2.set_title(\"Contour Plot of Total Current Consumption vs gm/ID Space\")\n", - "ax3.set_title(\"cout/ID6,8 vs gm/ID Space\")\n", - "ax4.set_title(\"OTA Gain vs gm/ID Space\")\n", - "plt.show()\n", - "print(\"\")\n", - "print(\"\")\n", - "print(\"The current consumption graphs are assuming a half-circuit model.\")\n", - "print(\"This current must be doubled to account for the entire current consumption of the OTA.\")\n", - "print(\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "632c0fae-f2b5-4a04-ae81-e3778ce4d320", - "metadata": {}, - "source": [ - "\n", - "**In the contour plot above the region marked with purple represents where the variation across temperature is less than 10%. The hatched out regions show where the specifications are not met and solution space is not valid. Below the graphs are edited slightly for clarity and and to account for the full circuit model by doubling current consumption of half circuit model (above is half circuit current consumption).**\n", - "\n", - "\"Design" - ] - }, - { - "cell_type": "markdown", - "id": "defe6ae6-2664-4ff2-8812-5f50b93fa53f", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## **Design Choice for Process Variation and Temperature**\n", - "\n", - "To size the devices in the OTA the gm/ID optimal values can be found with these charts above.\n", - "\n", - "**A gm/ID value of 16 for the NFET devices and 5.25 for PFET devices is chosen**.\n", - "\n", - "This example uses the Skywater 130 nm-A open source process and PDK with characterization done on the 500 nm channel length 1.8V Regular-VTH devices. The above figures shows where the temperature variation across the design space is less than 10\\% for a gain bandwidth product of 100Mhz and a 4pF capacitive load. To ensure functionality across temperature without a wide variation in current, this region should be chosen as the design solution subspace. In this region the minimum current consumption is where the gm/ID values for the PFETS approaches 0. This solution is infeasible for multiple reasons. The first reason begin extremely low gm/ID requires extremely high current density, which for a fixed current, this requires smaller devices than what is available in the PDK. Extremely small devices are also susceptible to high mismatch. The second reason is that a very large voltage drop Vds across the drain source terminals of the device is required for very low gm/ID. This both limits the available swing of the OTA and can also exceed the limits of the 1.8V device. For these reasons, gm/ID of the PFET devices is chosen to be 5.25 as this value represents a balance between power optimization and available headroom. gm/ID for the NFET devices is simply chosen to be the maximum value within the 10\\% temperature variation boundary.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "c3d2ee59-d7cb-450d-bd9d-599ca32c2353", - "metadata": {}, - "source": [ - "## **Sizing Devices from gm/ID Values**\n", - "\n", - "To size the devices from gm/ID values, current density charts/lookup is used.\n", - "\n", - "Since the current through all devices is known along with the gm/ID values, the width of a device can be calculated as:\n", - "\n", - "**Device Width = Device Current / Current Density**\n", - "\n", - "Current density is a function of gm/ID and can be looked up as such. Below is a plot for current density for the NFET and PFET devices.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "e30a1cd1-be72-473a-a2f0-f579ee999e81", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#\n", - "# Create graphs and plot current density for NFET and PFET devices with Length = 500n\n", - "#\n", - "\n", - "fig = plt.figure(figsize=(14, 10))\n", - "ax1 = fig.add_subplot(221)\n", - "ax2 = fig.add_subplot(222)\n", - "\n", - "nfet_cold.plot_processes_params(\"kgm\", \"iden\", show_plot=False, new_plot=False, fig1=fig, ax1=ax1, color=\"blue\", legend_str=\"L = 500nm, T = -25° C\")\n", - "nfet_nominal.plot_processes_params(\"kgm\", \"iden\", show_plot=False, new_plot=False, fig1=fig, ax1=ax1, color=\"green\", legend_str=\"L = 500nm, T = 25° C\")\n", - "nfet_hot.plot_processes_params(\"kgm\", \"iden\", show_plot=False, new_plot=False, fig1=fig, ax1=ax1, color=\"red\", legend_str=\"L = 500nm, T = 75° C\")\n", - "\n", - "pfet_cold.plot_processes_params(\"kgm\", \"iden\", show_plot=False, color=\"blue\", new_plot=False, fig1=fig, ax1=ax2, legend_str=\"L = 500nm, T = -25° C\")\n", - "pfet_nominal.plot_processes_params(\"kgm\", \"iden\", show_plot=False, color=\"green\", new_plot=False, fig1=fig, ax1=ax2, legend_str=\"L = 500nm, T = 25° C\")\n", - "pfet_hot.plot_processes_params(\"kgm\", \"iden\", show_plot=False, color=\"red\", new_plot=False, fig1=fig, ax1=ax2, legend_str=\"L = 500nm, T = 75° C\")\n", - "\n", - "ax1.set_yscale(\"log\")\n", - "ax1.set_title(\"1v8 Regular Threshold NFET TT Current Density\")\n", - "ax1.set_xlabel(\"gm/ID [1/V]\")\n", - "ax1.set_ylabel(\"Current Density [A/m]\")\n", - "\n", - "ax2.set_yscale(\"log\")\n", - "ax2.set_title(\"1v8 Regular Threshold PFET TT Current Density\")\n", - "ax2.set_xlabel(\"gm/ID [1/V]\")\n", - "ax2.set_ylabel(\"Current Density [A/m]\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "8a5145fb-cdbc-49fa-b9a2-e28b3239a3ab", - "metadata": {}, - "source": [ - "\n", - "**It can be observed in the figure for the PFET current density that the current density exhibits behavior that is not physically possible. This issue is related to Skywater's PFET modeling and is documented here https://github.com/google/skywater-pdk/issues/381. Dr. Borris Murmann, Dr. Harald Pretl, and myself (Alec Adair) have all commented on in September 2022 - my comments are near the end of the thread.**\n", - "\n", - "For proper design using the Skywater130nm-A PDK, PFET gm/ID values of less than about 15 should be used to ensure accurate results.\n", - "\n", - "---\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "29a715b1-fc03-4a02-b19c-3d2a3a54f197", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Example run of size_ota_devices_from_kgm_and_currents\n", - "\n", - "NFET Current Density for gm/ID=15.00: 2.25 A/m\n", - "PFET Current Density for gm/ID=5.25: 7.25 A/m\n", - "\n", - "M1,2 Width: 11.75 um\n", - "M3,4 Width: 3.65 um\n", - "M5,6 Width: 80.58 um\n", - "M7,8 Width: 25.01 um\n" - ] - } - ], - "source": [ - "\n", - "# Function calculates device width from gm/ID values and OTA Specifications\n", - "# Returns device widths\n", - "def size_ota_devices_from_kgm_and_currents(n_corner, p_corner, kgm_n, kgm_p, gain=50, bw=2e6, cload=4e-12, phase_margin=60):\n", - " gbw = gain*bw\n", - " ninety_minus_pm = 90 - phase_margin\n", - " therm_noise = 500e-9\n", - " tan_pm = math.tan(ninety_minus_pm*math.pi/180)\n", - " alpha = 1/tan_pm\n", - " two_pi_alpha_gbw = 2*math.pi*alpha*gbw\n", - " f2 = alpha*gbw\n", - " total_current, m1_current, m6_current, beta_i_j, kcout_i_j, gain_i_j, thermal_rms_noise_i_j, beta_valid_i_j, gain_valid, thermal_noise_valid, kc_out = total_current_ota(n_corner, p_corner, kgm_n, kgm_p, \n", - " alpha, gbw, cload, gain_spec=gain, thermal_noise_spec=therm_noise)\n", - " iden1_2 = n_corner.lookup(param1=\"kgm\", param2=\"iden\", param1_val=kgm_n)\n", - " iden3_4 = p_corner.lookup(param1=\"kgm\", param2=\"iden\", param1_val=kgm_p)\n", - " print(\"\")\n", - " #print(\"NFET Current Density for gm/ID=\" + str(kgm_n) + \": \" + str(iden1_2) + \" A/m\")\n", - " #print(\"PFET Current Density for gm/ID=\" + str(kgm_p) + \": \" + str(iden3_4) + \" A/m\")\n", - " print(f\"NFET Current Density for gm/ID={kgm_n:.2f}: {iden1_2:.2f} A/m\")\n", - " print(f\"PFET Current Density for gm/ID={kgm_p:.2f}: {iden3_4:.2f} A/m\")\n", - " print(\"\")\n", - " w1_2 = m1_current/iden1_2\n", - " w3_4 = m1_current/iden3_4\n", - " w5_6 = m6_current/iden1_2\n", - " w7_8 = m6_current/iden3_4\n", - " print(f\"M1,2 Width: {w1_2*1e6:.2f} um\")\n", - " print(f\"M3,4 Width: {w3_4*1e6:.2f} um\")\n", - " print(f\"M5,6 Width: {w5_6*1e6:.2f} um\")\n", - " print(f\"M7,8 Width: {w7_8*1e6:.2f} um\")\n", - " return w1_2, w3_4, w5_6, w7_8\n", - "\n", - "# Example for running sizing function for transistors \n", - "print(\"Example run of size_ota_devices_from_kgm_and_currents\")\n", - "w1_2, w3_4, w5_6, w7_8 = size_ota_devices_from_kgm_and_currents(nfet_nominal, pfet_nominal, kgm_n=15, kgm_p=5.255)\n" - ] - }, - { - "cell_type": "markdown", - "id": "89c77a35-bfdd-456a-9471-6c95f08eb041", - "metadata": {}, - "source": [ - "---\n", - "## **Design Choices for Layout**\n", - "\n", - "To achieve a fixed gm/ID value across temperature corners for a fixed bias current, the current density of a device needs to be adjusted accordingly. This can be done by adjusting the width of a device. With a non-programmable layout, device width is fixed and cannot be adjustment cannot be done. For this work a layout is generated for each corner corresponding to the correct current density to achieve the fixed gm/ID values and currents in the design. A rise in temperature corresponds to a lower current density required to achieve a given gm/ID. This lower current density requirement then corresponds to a wider device and larger layout. Further work aims to address analytical and procedural layout and design convergence.\n", - "\n", - "**The ALIGN analog layout generator requires that all devices in a generated design have the same width and an even number of fingers. The minimum finger width for the ALIGN tool is 420 nm, which for an even numer of fingers corresponds to an 840 nm pitch. The ideal widths calculated from current density must be converted into number of fingers for ALIGN.** \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "0e5800d3-81ba-4a33-9cc1-b3106c03db4a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Example run of get_fingers_for_align from calculated widths\n", - "\n", - "M1,2 # of Fingers: 28\n", - "M3,4 # of Fingers: 10\n", - "M5,6 # of Fingers: 192\n", - "M7,8 # of Fingers: 60\n" - ] - } - ], - "source": [ - "def get_fingers_for_align(w1_2, w3_4, w5_6, w7_8):\n", - " # Define the pitch\n", - " pitch = 420e-9\n", - "\n", - " # Function to calculate the number of fingers\n", - " def calculate_fingers(width):\n", - " # Calculate the number of fingers\n", - " num_fingers = round(width / pitch)\n", - " # Ensure the number of fingers is even\n", - " if num_fingers % 2 != 0:\n", - " num_fingers += 1\n", - " return num_fingers\n", - "\n", - " # Calculate the number of fingers for each width\n", - " w1_2_fingers = calculate_fingers(w1_2)\n", - " w3_4_fingers = calculate_fingers(w3_4)\n", - " w5_6_fingers = calculate_fingers(w5_6)\n", - " w7_8_fingers = calculate_fingers(w7_8)\n", - " print(\"\")\n", - " print(\"M1,2 # of Fingers: \" + str(w1_2_fingers))\n", - " print(\"M3,4 # of Fingers: \" + str(w3_4_fingers))\n", - " print(\"M5,6 # of Fingers: \" + str(w5_6_fingers))\n", - " print(\"M7,8 # of Fingers: \" + str(w7_8_fingers))\n", - " return w1_2_fingers, w3_4_fingers, w5_6_fingers, w7_8_fingers\n", - "\n", - "# Example for running sizing function for transistors \n", - "print(\"Example run of get_fingers_for_align from calculated widths\")\n", - "f1_2, f3_4, f5_6, f7_8 = get_fingers_for_align(w1_2, w3_4, w5_6, w7_8)\n" - ] - }, - { - "cell_type": "markdown", - "id": "e22ae56c-235b-4cce-9a91-ae98ca680022", - "metadata": {}, - "source": [ - "---\n", - "## **Netlist Generation**\n", - "\n", - "**Create Netlists Each Optimized for The Three Different Temperatures -25°, 25°, and 75° Celsius**\n", - "\n", - "At this stage, netlists for SPICE simulation and ALIGN layout can be generated." - ] - }, - { - "cell_type": "markdown", - "id": "236837a6-0e19-4fde-98c7-b95ea9597d95", - "metadata": {}, - "source": [ - "\n", - "**Spice Netlist Generation**\n", - "\n", - "In the design directory (located at in the same path as this notebook) there is a subdirectory for all simulation data. Within this directory there are three directories spice_-25c, spice_25c, and spice_75c. These directories contain spice testbenches as well as the generated spice netlists.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "1ad00200-8b95-4499-99fe-ed4e263e28e7", - "metadata": {}, - "outputs": [], - "source": [ - "# Creates SPICE netlist from a netlist template file with the given values for w1_2, w3_4, w5_6, w7_8.\n", - "# Replaces placeholders for W1-2, W3-4, W5-6, W7-8 and calculates W9-10 as twice W1-2.\n", - "def create_spice_netlist(netlist_template, output_netlist, w1_2, w3_4, w5_6, w7_8):\n", - " # Read the template ALIGN netlist file\n", - " with open(netlist_template, 'r') as file:\n", - " netlist = file.readlines()\n", - "\n", - " # Convert to micrometers\n", - " w1_2 = w1_2*1e6\n", - " w3_4 = w3_4*1e6\n", - " w5_6 = w5_6*1e6\n", - " w7_8 = w7_8*1e6\n", - "\n", - " #Round widths to two decimal places\n", - " w1_2 = round(w1_2, 2)\n", - " w3_4 = round(w3_4, 2)\n", - " w5_6 = round(w5_6, 2)\n", - " w7_8 = round(w7_8, 2)\n", - " \n", - " # Calculate NF9-10 as twice NF1-2\n", - " w9_10 = w1_2 * 2\n", - "\n", - " # Replace placeholders in the netlist template\n", - " updated_netlist = []\n", - " for line in netlist:\n", - " line = line.replace('$W1-2', str(w1_2))\n", - " line = line.replace('$W3-4', str(w3_4))\n", - " line = line.replace('$W5-6', str(w5_6))\n", - " line = line.replace('$W7-8', str(w7_8))\n", - " line = line.replace('$W9-10', str(w9_10))\n", - " updated_netlist.append(line)\n", - "\n", - " # Write the updated ALIGN netlist to a new file\n", - " with open(output_netlist, 'w') as file:\n", - " file.writelines(updated_netlist)\n", - " print(\"\")\n", - " print(\"SPICE Netlist output written to \" + output_netlist)\n", - " print(\"--------------\")\n", - " print(''.join(updated_netlist))" - ] - }, - { - "cell_type": "markdown", - "id": "32f1ef5a-a91f-45af-a3ac-ba7d5a4a26b0", - "metadata": {}, - "source": [ - "**SPICE Netlist Generation for -25° Celsius**" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "25fa961e-d473-41bb-ba36-25db701a434d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "NFET Current Density for gm/ID=15.00: 3.00 A/m\n", - "PFET Current Density for gm/ID=5.25: 8.50 A/m\n", - "\n", - "M1,2 Width: 7.49 um\n", - "M3,4 Width: 2.64 um\n", - "M5,6 Width: 59.49 um\n", - "M7,8 Width: 21.00 um\n", - "\n", - "SPICE Netlist output written to CM-OTA-Synthesis/design/simulation/spice_-25c/cm_ota_params.sp\n", - "--------------\n", - ".param w1_2=7.49\n", - ".param w3_4=2.64\n", - ".param w5_6=59.49\n", - ".param w7_8=21.0\n", - ".param w9_10=14.98\n", - ".param beta=1\n", - ".param nf1_2=1\n", - ".param nf3_4=1\n", - ".param nf5_6=1\n", - ".param nf7_8=1\n", - ".param nf9_10=1\n", - "\n", - ".param iref_ideal=65u\n", - ".param iref_post_layout=70u\n", - "\n" - ] - } - ], - "source": [ - "# Size OTA Devices using -25° C lookup tables with optimized gm/ID values \n", - "w1_2, w3_4, w5_6, w7_8 = size_ota_devices_from_kgm_and_currents(nfet_cold, pfet_cold, kgm_n=15, kgm_p=5.255)\n", - "\n", - "# Define SPICE netlist template file and output netlist to be generated\n", - "netlist_template = \"CM-OTA-Synthesis/design/simulation/cm_ota_params_template.sp\"\n", - "spice_netlist = \"CM-OTA-Synthesis/design/simulation/spice_-25c/cm_ota_params.sp\"\n", - "\n", - "# Generate SPICE netlist for -25° Temperature\n", - "create_spice_netlist(netlist_template, spice_netlist, w1_2, w3_4, w5_6, w7_8)\n" - ] - }, - { - "cell_type": "markdown", - "id": "3011480e-e089-47a9-950c-a3ff97e8ef37", - "metadata": {}, - "source": [ - "**SPICE Netlist Generation for 25° Celsius**" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "a40801dd-91bf-46fe-a4c2-3c0a272f5658", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "NFET Current Density for gm/ID=15.00: 2.25 A/m\n", - "PFET Current Density for gm/ID=5.25: 7.25 A/m\n", - "\n", - "M1,2 Width: 11.75 um\n", - "M3,4 Width: 3.65 um\n", - "M5,6 Width: 80.58 um\n", - "M7,8 Width: 25.01 um\n", - "\n", - "SPICE Netlist output written to CM-OTA-Synthesis/design/simulation/spice_25c/cm_ota_params.sp\n", - "--------------\n", - ".param w1_2=11.75\n", - ".param w3_4=3.65\n", - ".param w5_6=80.58\n", - ".param w7_8=25.01\n", - ".param w9_10=23.5\n", - ".param beta=1\n", - ".param nf1_2=1\n", - ".param nf3_4=1\n", - ".param nf5_6=1\n", - ".param nf7_8=1\n", - ".param nf9_10=1\n", - "\n", - ".param iref_ideal=65u\n", - ".param iref_post_layout=70u\n", - "\n" - ] - } - ], - "source": [ - "# Size OTA Devices using 25° C lookup tables with optimized gm/ID values \n", - "w1_2, w3_4, w5_6, w7_8 = size_ota_devices_from_kgm_and_currents(nfet_nominal, pfet_nominal, kgm_n=15, kgm_p=5.255)\n", - "\n", - "# Define SPICE netlist template file and output netlist to be generated\n", - "netlist_template = \"CM-OTA-Synthesis/design/simulation/cm_ota_params_template.sp\"\n", - "spice_netlist = \"CM-OTA-Synthesis/design/simulation/spice_25c/cm_ota_params.sp\"\n", - "\n", - "# Generate SPICE netlist for 25° Temperature\n", - "create_spice_netlist(netlist_template, spice_netlist, w1_2, w3_4, w5_6, w7_8)\n" - ] - }, - { - "cell_type": "markdown", - "id": "173c1169-c941-4e84-bb1e-c9996e96e90f", - "metadata": {}, - "source": [ - "**SPICE Netlist Generation for 75° Celsius**" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "906f95b5-12f5-42ab-a2ff-a324feccbd3b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "NFET Current Density for gm/ID=15.00: 1.50 A/m\n", - "PFET Current Density for gm/ID=5.25: 6.25 A/m\n", - "\n", - "M1,2 Width: 20.70 um\n", - "M3,4 Width: 4.97 um\n", - "M5,6 Width: 124.44 um\n", - "M7,8 Width: 29.88 um\n", - "\n", - "SPICE Netlist output written to CM-OTA-Synthesis/design/simulation/spice_75c/cm_ota_params.sp\n", - "--------------\n", - ".param w1_2=20.7\n", - ".param w3_4=4.97\n", - ".param w5_6=124.44\n", - ".param w7_8=29.88\n", - ".param w9_10=41.4\n", - ".param beta=1\n", - ".param nf1_2=1\n", - ".param nf3_4=1\n", - ".param nf5_6=1\n", - ".param nf7_8=1\n", - ".param nf9_10=1\n", - "\n", - ".param iref_ideal=65u\n", - ".param iref_post_layout=70u\n", - "\n" - ] - } - ], - "source": [ - "# Size OTA Devices using 75° C lookup tables with optimized gm/ID values \n", - "w1_2, w3_4, w5_6, w7_8 = size_ota_devices_from_kgm_and_currents(nfet_hot, pfet_hot, kgm_n=15, kgm_p=5.255)\n", - "\n", - "# Define SPICE netlist template file and output netlist to be generated\n", - "netlist_template = \"CM-OTA-Synthesis/design/simulation/cm_ota_params_template.sp\"\n", - "spice_netlist = \"CM-OTA-Synthesis/design/simulation/spice_75c/cm_ota_params.sp\"\n", - "\n", - "# Generate SPICE netlist for 25° Temperature\n", - "create_spice_netlist(netlist_template, spice_netlist, w1_2, w3_4, w5_6, w7_8)\n" - ] - }, - { - "cell_type": "markdown", - "id": "898db231-09a1-4401-9080-431055193f21", - "metadata": {}, - "source": [ - "---\n", - "\n", - "**ALIGN Netlist Generation**\n", - "\n", - "In the design directory (located at in the same path as this notebook) there are 3 subdirectories called gds_-25c, gds_25c, and gds_75c. These directories each contain a subdirectory cm_ota_align with one more subdirectory align_input. The align_input directory contains the ALIGN inputs files to generate layout for an OTA. ALIGN takes two inputs first, an ALIGN netlist with device sizes and net connections, and second a constraints JSON file to direct placement of cells. The same constraints file is used for all layout generation and is the following.\n", - "\n", - "**ALIGN Constraint File for CM OTA**" - ] - }, - { - "cell_type": "raw", - "id": "1dde0026-1f37-4366-aab5-589813e408e1", - "metadata": {}, - "source": [ - "[\n", - " {\"constraint\": \"PowerPorts\", \"ports\": [\"vdd\"]},\n", - " {\"constraint\": \"GroundPorts\", \"ports\": [\"vss\"]},\n", - " {\"constraint\": \"SymmetricBlocks\", \"pairs\":[[\"M1\",\"M2\"], [\"M3\",\"M4\"], [\"M5\",\"M6\"], [\"M7\",\"M8\"], [\"M9\",\"M10\"]], \"direction\" : \"V\"},\n", - " {\"constraint\": \"CompactPlacement\", \"style\": \"center\"},\n", - " {\"constraint\": \"ConfigureCompiler\", \"is_digital\": false}\n", - "]\n" - ] - }, - { - "cell_type": "markdown", - "id": "1dbc3c96-0e6c-4fb7-a22a-3786b5fd38e4", - "metadata": {}, - "source": [ - "**Constraints Explanation**\n", - "\n", - "The first and second constraint sets the power and ground ports for the block to be named vdd and vss.\n", - "\n", - "The third constraint directs the placement engine to ensure symmetry for the transistor pairs in the design in the vertical direction.\n", - "\n", - "The fourth constraint directs the placement engine to do a compact placement around the centered around the middle of the design.\n", - "\n", - "The fifth constraint tells ALIGN that the block is an analog block and not a digital one.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "da2af49a-3d6f-4963-b79a-c4331df69482", - "metadata": {}, - "source": [ - "**Definition of function to generate an ALIGN netlist from finger count**\n", - "\n", - "Generate an ALIGN netlist from the OTA ALIGN netlist template in the design directory of this notebook and output to respective gds/cm_ota_align/align_inputs directory." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "fa4c40ff-4fb4-4a37-b5a6-d5f0b9165851", - "metadata": {}, - "outputs": [], - "source": [ - "# Creates ALIGN netlist file with the given values for nf1_2, nf3_4, nf5_6, nf7_8.\n", - "# Replaces placeholders for NF1-2, NF3-4, NF5-6, NF7-8 and calculates NF9-10 as twice NF1-2.\n", - "# The netlist_template is netlist to e \n", - "def create_align_netlist(netlist_template, output_netlist, nf1_2, nf3_4, nf5_6, nf7_8):\n", - " # Read the template ALIGN netlist file\n", - " with open(netlist_template, 'r') as file:\n", - " netlist = file.readlines()\n", - "\n", - " # Calculate NF9-10 as twice NF1-2\n", - " nf9_10 = nf1_2 * 2\n", - "\n", - " # Replace placeholders in the netlist template\n", - " updated_netlist = []\n", - " for line in netlist:\n", - " line = line.replace('$NF1-2', str(nf1_2))\n", - " line = line.replace('$NF3-4', str(nf3_4))\n", - " line = line.replace('$NF5-6', str(nf5_6))\n", - " line = line.replace('$NF7-8', str(nf7_8))\n", - " line = line.replace('$NF9-10', str(nf9_10))\n", - " updated_netlist.append(line)\n", - "\n", - " # Write the updated ALIGN netlist to a new file\n", - " with open(output_netlist, 'w') as file:\n", - " file.writelines(updated_netlist)\n", - " print(\"\")\n", - " print(\"ALIGN Netlist output written to \" + output_netlist)\n", - " print(\"--------------\")\n", - " print(''.join(updated_netlist))\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "6bf5deb2-809d-4a5b-814e-d92a284ac556", - "metadata": {}, - "source": [ - "**ALIGN Netlist Generation for -25° Celsius**" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "6f324d9c-f529-4b2b-bbe5-4399708e5020", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "NFET Current Density for gm/ID=15.00: 3.00 A/m\n", - "PFET Current Density for gm/ID=5.25: 8.50 A/m\n", - "\n", - "M1,2 Width: 7.49 um\n", - "M3,4 Width: 2.64 um\n", - "M5,6 Width: 59.49 um\n", - "M7,8 Width: 21.00 um\n", - "\n", - "M1,2 # of Fingers: 18\n", - "M3,4 # of Fingers: 6\n", - "M5,6 # of Fingers: 142\n", - "M7,8 # of Fingers: 50\n", - "\n", - "ALIGN Netlist output written to CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/align_input/current_mirror_ota.sp\n", - "--------------\n", - ".subckt current_mirror_ota vss vdd vout vinn vinp id\n", - "M10 id id vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=36\n", - "M9 source id vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=36\n", - "M1 ds1 vinn source vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=18\n", - "M2 ds2 vinp source vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=18\n", - "M3 ds1 ds1 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=6\n", - "M4 ds2 ds2 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=6\n", - "M5 ds3 ds3 vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=142\n", - "M6 vout ds3 vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=142\n", - "M7 ds3 ds1 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=50\n", - "M8 vout ds2 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=50\n", - ".ends current_mirror_ota\n", - "\n" - ] - } - ], - "source": [ - "# Size OTA Devices using -25° C lookup tables with optimized gm/ID values \n", - "w1_2, w3_4, w5_6, w7_8 = size_ota_devices_from_kgm_and_currents(nfet_cold, pfet_cold, kgm_n=15, kgm_p=5.255)\n", - "\n", - "# Convert Widths to unit fingers for align\n", - "nf1_2, nf3_4, nf5_6, nf7_8 = get_fingers_for_align(w1_2, w3_4, w5_6, w7_8)\n", - "\n", - "# Define ALIGN netlist template file and output netlist to be generated\n", - "netlist_template = \"CM-OTA-Synthesis/design/align_netlist_template.txt\"\n", - "align_netlist = \"CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/align_input/current_mirror_ota.sp\"\n", - "\n", - "# Generate ALIGN netlist for -25° Temperature\n", - "create_align_netlist(netlist_template, align_netlist, nf1_2, nf3_4, nf5_6, nf7_8)\n" - ] - }, - { - "cell_type": "markdown", - "id": "e41c16c0-b96c-4426-b91e-cb99fba4f037", - "metadata": {}, - "source": [ - "**ALIGN Netlist Generation for 25° Celsius**" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "889020b8-7a79-457e-a54d-86ee78935135", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "NFET Current Density for gm/ID=15.00: 2.25 A/m\n", - "PFET Current Density for gm/ID=5.25: 7.25 A/m\n", - "\n", - "M1,2 Width: 11.75 um\n", - "M3,4 Width: 3.65 um\n", - "M5,6 Width: 80.58 um\n", - "M7,8 Width: 25.01 um\n", - "\n", - "M1,2 # of Fingers: 28\n", - "M3,4 # of Fingers: 10\n", - "M5,6 # of Fingers: 192\n", - "M7,8 # of Fingers: 60\n", - "\n", - "ALIGN Netlist output written to CM-OTA-Synthesis/design/gds_25c/cm_ota_align/align_input/current_mirror_ota.sp\n", - "--------------\n", - ".subckt current_mirror_ota vss vdd vout vinn vinp id\n", - "M10 id id vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=56\n", - "M9 source id vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=56\n", - "M1 ds1 vinn source vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=28\n", - "M2 ds2 vinp source vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=28\n", - "M3 ds1 ds1 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=10\n", - "M4 ds2 ds2 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=10\n", - "M5 ds3 ds3 vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=192\n", - "M6 vout ds3 vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=192\n", - "M7 ds3 ds1 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=60\n", - "M8 vout ds2 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=60\n", - ".ends current_mirror_ota\n", - "\n" - ] - } - ], - "source": [ - "# Size OTA Devices using 25° C lookup tables with optimized gm/ID values \n", - "w1_2, w3_4, w5_6, w7_8 = size_ota_devices_from_kgm_and_currents(nfet_nominal, pfet_nominal, kgm_n=15, kgm_p=5.255)\n", - "\n", - "# Convert Widths to unit fingers for align\n", - "nf1_2, nf3_4, nf5_6, nf7_8 = get_fingers_for_align(w1_2, w3_4, w5_6, w7_8)\n", - "\n", - "# Define ALIGN netlist template file and output netlist to be generated\n", - "netlist_template = \"CM-OTA-Synthesis/design/align_netlist_template.txt\"\n", - "align_netlist = \"CM-OTA-Synthesis/design/gds_25c/cm_ota_align/align_input/current_mirror_ota.sp\"\n", - "\n", - "# Generate ALIGN netlist for -25° Temperature\n", - "create_align_netlist(netlist_template, align_netlist, nf1_2, nf3_4, nf5_6, nf7_8)\n" - ] - }, - { - "cell_type": "markdown", - "id": "4ef31bcf-20ce-410d-acc3-125716f5419b", - "metadata": {}, - "source": [ - "**ALIGN Netlist Generation for 75° Celsius**" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "6371c7a9-e7f1-45f0-9b80-ed2ae7a51275", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "NFET Current Density for gm/ID=15.00: 1.50 A/m\n", - "PFET Current Density for gm/ID=5.25: 6.25 A/m\n", - "\n", - "M1,2 Width: 20.70 um\n", - "M3,4 Width: 4.97 um\n", - "M5,6 Width: 124.44 um\n", - "M7,8 Width: 29.88 um\n", - "\n", - "M1,2 # of Fingers: 50\n", - "M3,4 # of Fingers: 12\n", - "M5,6 # of Fingers: 296\n", - "M7,8 # of Fingers: 72\n", - "\n", - "ALIGN Netlist output written to CM-OTA-Synthesis/design/gds_75c/cm_ota_align/align_input/current_mirror_ota.sp\n", - "--------------\n", - ".subckt current_mirror_ota vss vdd vout vinn vinp id\n", - "M10 id id vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=100\n", - "M9 source id vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=100\n", - "M1 ds1 vinn source vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=50\n", - "M2 ds2 vinp source vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=50\n", - "M3 ds1 ds1 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=12\n", - "M4 ds2 ds2 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=12\n", - "M5 ds3 ds3 vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=296\n", - "M6 vout ds3 vss vss sky130_fd_pr__nfet_01v8 L=500e-9 w=4.2e-7 nf=296\n", - "M7 ds3 ds1 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=72\n", - "M8 vout ds2 vdd vdd sky130_fd_pr__pfet_01v8 L=500e-9 w=4.2e-7 nf=72\n", - ".ends current_mirror_ota\n", - "\n" - ] - } - ], - "source": [ - "# Size OTA Devices using 25° C lookup tables with optimized gm/ID values \n", - "w1_2, w3_4, w5_6, w7_8 = size_ota_devices_from_kgm_and_currents(nfet_hot, pfet_hot, kgm_n=15, kgm_p=5.255)\n", - "\n", - "# Convert Widths to unit fingers for align\n", - "nf1_2, nf3_4, nf5_6, nf7_8 = get_fingers_for_align(w1_2, w3_4, w5_6, w7_8)\n", - "\n", - "# Define ALIGN netlist template file and output netlist to be generated\n", - "netlist_template = \"CM-OTA-Synthesis/design/align_netlist_template.txt\"\n", - "align_netlist = \"CM-OTA-Synthesis/design/gds_75c/cm_ota_align/align_input/current_mirror_ota.sp\"\n", - "\n", - "# Generate ALIGN netlist for -25° Temperature\n", - "create_align_netlist(netlist_template, align_netlist, nf1_2, nf3_4, nf5_6, nf7_8)\n" - ] - }, - { - "cell_type": "markdown", - "id": "31bce5e6-6350-4499-ac55-0093013fc372", - "metadata": {}, - "source": [ - "---\n", - "## **Generate Current Mirror Layout With ALIGN**\n", - "\n", - "Call the ALIGN layout generator and generate layouts from the generated netlists.\n", - "\n", - "**GDS outputs are put into the CM-OTA-Synthesis/design/gds_TEMPERATURES/cm_ota_align directories**" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "7a25236b-99d6-414a-9436-bfb700f4b193", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32malign.main INFO : Reading netlist: /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/align_input/current_mirror_ota.sp subckt=CURRENT_MIRROR_OTA, flat=0\u001b[0m\n", - "\u001b[32malign.compiler.compiler INFO : Starting topology identification...\u001b[0m\n", - "\u001b[32malign.compiler.user_const INFO : Reading constraint file: [PosixPath('/home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/align_input/current_mirror_ota.const.json')]\u001b[0m\n", - "\u001b[32malign.compiler.compiler INFO : Completed topology identification.\u001b[0m\n", - "\u001b[32malign.pnr.main INFO : Running Place & Route for CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32malign.pnr.build_pnr_model INFO : Reading contraint json file CURRENT_MIRROR_OTA.pnr.const.json\u001b[0m\n", - "\u001b[32malign.pnr.build_pnr_model INFO : Reading contraint json file CURRENT_MIRROR_OTA.pnr.const.json\u001b[0m\n", - "\u001b[32malign.pnr.placer INFO : Starting bottom-up placement on CURRENT_MIRROR_OTA 0\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : Required 1 perturbations to generate a feasible solution.\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 10 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 20 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 30 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 40 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 50 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 60 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 70 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 80 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 90 %\u001b[0m\n", - "\u001b[32malign.pnr.build_pnr_model INFO : Reading contraint json file CURRENT_MIRROR_OTA.pnr.const.json\u001b[0m\n", - "\u001b[32malign.pnr.router INFO : Starting top_down routing on CURRENT_MIRROR_OTA 0 restricted to None\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : GcellGlobalRouter: CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : GcellDetailRouter: CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : Create power grid: CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : Power routing CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32malign.pnr.main INFO : OUTPUT json at /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/3_pnr/CURRENT_MIRROR_OTA_0.json\u001b[0m\n", - "\u001b[32malign.pnr.main INFO : OUTPUT gds.json /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/3_pnr/CURRENT_MIRROR_OTA_0.python.gds.json\u001b[0m\n", - "Use KLayout to visualize the generated GDS: /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/CURRENT_MIRROR_OTA_0.gds\n", - "Use KLayout to visualize the python generated GDS: /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/CURRENT_MIRROR_OTA_0.python.gds\n", - "\n", - "LEF file created at ./CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/CURRENT_MIRROR_OTA_0.lef\n", - "GDS file created at ./CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/CURRENT_MIRROR_OTA_0.gds\n" - ] - } - ], - "source": [ - "!schematic2layout.py CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/align_input -p CM-OTA-Synthesis/eda/ALIGN-pdk-sky130/SKY130_PDK/ -w CM-OTA-Synthesis/design/gds_-25c/cm_ota_align\n", - "print(\"\")\n", - "print(\"LEF file created at ./CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/CURRENT_MIRROR_OTA_0.lef\")\n", - "print(\"GDS file created at ./CM-OTA-Synthesis/design/gds_-25c/cm_ota_align/CURRENT_MIRROR_OTA_0.gds\")" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "5d112562-a420-4c54-9630-171f49745657", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32malign.main INFO : Reading netlist: /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_25c/cm_ota_align/align_input/current_mirror_ota.sp subckt=CURRENT_MIRROR_OTA, flat=0\u001b[0m\n", - "\u001b[32malign.compiler.compiler INFO : Starting topology identification...\u001b[0m\n", - "\u001b[32malign.compiler.user_const INFO : Reading constraint file: [PosixPath('/home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_25c/cm_ota_align/align_input/current_mirror_ota.const.json')]\u001b[0m\n", - "\u001b[32malign.compiler.compiler INFO : Completed topology identification.\u001b[0m\n", - "\u001b[32malign.pnr.main INFO : Running Place & Route for CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32malign.pnr.build_pnr_model INFO : Reading contraint json file CURRENT_MIRROR_OTA.pnr.const.json\u001b[0m\n", - "\u001b[32malign.pnr.build_pnr_model INFO : Reading contraint json file CURRENT_MIRROR_OTA.pnr.const.json\u001b[0m\n", - "\u001b[32malign.pnr.placer INFO : Starting bottom-up placement on CURRENT_MIRROR_OTA 0\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : Required 1 perturbations to generate a feasible solution.\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 10 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 20 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 30 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 40 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 50 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 60 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 70 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 80 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 90 %\u001b[0m\n", - "\u001b[32malign.pnr.build_pnr_model INFO : Reading contraint json file CURRENT_MIRROR_OTA.pnr.const.json\u001b[0m\n", - "\u001b[32malign.pnr.router INFO : Starting top_down routing on CURRENT_MIRROR_OTA 0 restricted to None\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : GcellGlobalRouter: CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : GcellDetailRouter: CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : Create power grid: CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : Power routing CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32malign.pnr.main INFO : OUTPUT json at /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_25c/cm_ota_align/3_pnr/CURRENT_MIRROR_OTA_0.json\u001b[0m\n", - "\u001b[32malign.pnr.main INFO : OUTPUT gds.json /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_25c/cm_ota_align/3_pnr/CURRENT_MIRROR_OTA_0.python.gds.json\u001b[0m\n", - "Use KLayout to visualize the generated GDS: /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_25c/cm_ota_align/CURRENT_MIRROR_OTA_0.gds\n", - "Use KLayout to visualize the python generated GDS: /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_25c/cm_ota_align/CURRENT_MIRROR_OTA_0.python.gds\n", - "\n", - "LEF file created at ./CM-OTA-Synthesis/design/gds_25c/cm_ota_align/CURRENT_MIRROR_OTA_0.lef\n", - "GDS file created at ./CM-OTA-Synthesis/design/gds_25c/cm_ota_align/CURRENT_MIRROR_OTA_0.gds\n" - ] - } - ], - "source": [ - "!schematic2layout.py CM-OTA-Synthesis/design/gds_25c/cm_ota_align/align_input -p CM-OTA-Synthesis/eda/ALIGN-pdk-sky130/SKY130_PDK/ -w CM-OTA-Synthesis/design/gds_25c/cm_ota_align\n", - "print(\"\")\n", - "print(\"LEF file created at ./CM-OTA-Synthesis/design/gds_25c/cm_ota_align/CURRENT_MIRROR_OTA_0.lef\")\n", - "print(\"GDS file created at ./CM-OTA-Synthesis/design/gds_25c/cm_ota_align/CURRENT_MIRROR_OTA_0.gds\")" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "f2074244-6cf7-4dff-bd88-aecbfb01d108", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32malign.main INFO : Reading netlist: /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_75c/cm_ota_align/align_input/current_mirror_ota.sp subckt=CURRENT_MIRROR_OTA, flat=0\u001b[0m\n", - "\u001b[32malign.compiler.compiler INFO : Starting topology identification...\u001b[0m\n", - "\u001b[32malign.compiler.user_const INFO : Reading constraint file: [PosixPath('/home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_75c/cm_ota_align/align_input/current_mirror_ota.const.json')]\u001b[0m\n", - "\u001b[32malign.compiler.compiler INFO : Completed topology identification.\u001b[0m\n", - "\u001b[32malign.pnr.main INFO : Running Place & Route for CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32malign.pnr.build_pnr_model INFO : Reading contraint json file CURRENT_MIRROR_OTA.pnr.const.json\u001b[0m\n", - "\u001b[32malign.pnr.build_pnr_model INFO : Reading contraint json file CURRENT_MIRROR_OTA.pnr.const.json\u001b[0m\n", - "\u001b[32malign.pnr.placer INFO : Starting bottom-up placement on CURRENT_MIRROR_OTA 0\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : Required 1 perturbations to generate a feasible solution.\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 10 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 20 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 30 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 40 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 50 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 60 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 70 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 80 %\u001b[0m\n", - "\u001b[32mPnR.placer.Placer.PlacementCoreAspectRatio_ILP INFO : ..... 90 %\u001b[0m\n", - "\u001b[32malign.pnr.build_pnr_model INFO : Reading contraint json file CURRENT_MIRROR_OTA.pnr.const.json\u001b[0m\n", - "\u001b[32malign.pnr.router INFO : Starting top_down routing on CURRENT_MIRROR_OTA 0 restricted to None\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : GcellGlobalRouter: CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : GcellDetailRouter: CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : Create power grid: CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32mPnR.router.Router.RouteWork INFO : Power routing CURRENT_MIRROR_OTA\u001b[0m\n", - "\u001b[32malign.pnr.main INFO : OUTPUT json at /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_75c/cm_ota_align/3_pnr/CURRENT_MIRROR_OTA_0.json\u001b[0m\n", - "\u001b[32malign.pnr.main INFO : OUTPUT gds.json /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_75c/cm_ota_align/3_pnr/CURRENT_MIRROR_OTA_0.python.gds.json\u001b[0m\n", - "Use KLayout to visualize the generated GDS: /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_75c/cm_ota_align/CURRENT_MIRROR_OTA_0.gds\n", - "Use KLayout to visualize the python generated GDS: /home/adair/Documents/CAD/sscs-ose-code-a-chip.github.io/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/CM-OTA-Synthesis/design/gds_75c/cm_ota_align/CURRENT_MIRROR_OTA_0.python.gds\n", - "\n", - "LEF file created at ./CM-OTA-Synthesis/design/gds_75c/cm_ota_align/CURRENT_MIRROR_OTA_0.lef\n", - "GDS file created at ./CM-OTA-Synthesis/design/gds_75c/cm_ota_align/CURRENT_MIRROR_OTA_0.gds\n" - ] - } - ], - "source": [ - "!schematic2layout.py CM-OTA-Synthesis/design/gds_75c/cm_ota_align/align_input -p CM-OTA-Synthesis/eda/ALIGN-pdk-sky130/SKY130_PDK/ -w CM-OTA-Synthesis/design/gds_75c/cm_ota_align\n", - "print(\"\")\n", - "print(\"LEF file created at ./CM-OTA-Synthesis/design/gds_75c/cm_ota_align/CURRENT_MIRROR_OTA_0.lef\")\n", - "print(\"GDS file created at ./CM-OTA-Synthesis/design/gds_75c/cm_ota_align/CURRENT_MIRROR_OTA_0.gds\")" - ] - }, - { - "cell_type": "markdown", - "id": "50a8f7d1-8d0d-4f9d-8dd4-b08935b88510", - "metadata": {}, - "source": [ - "## **GDS Renderings**\n", - "\n", - "\"GDS\"\n", - "\n", - "The three GDS renderings are for -25°, 25°, and 75° C generations from left to right." - ] - }, - { - "cell_type": "markdown", - "id": "534e0feb-8347-4742-822b-02d3dbe9dec2", - "metadata": {}, - "source": [ - "---\n", - "## **Physical Verification and Layout Extraction**\n", - "\n", - "Use the magic layout tool to run netlist extraction on the three layouts." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "54a0f9ce-0496-496f-830d-7030fdd31168", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Magic 8.3 revision 432 - Compiled on su 24.9.2023 15.09.29 +0300.\n", - "Starting magic under Tcl interpreter\n", - "Using the terminal as the console.\n", - "Using NULL graphics device.\n", - "Processing system .magicrc file\n", - "Loading \"CM-OTA-Synthesis/design/gds_-25c/extract_current_mirror_ota.tcl\" from command line.\n", - "Sourcing design .magicrc for technology sky130A ...\n", - "2 Magic internal units = 1 Lambda\n", - "Input style default: scaleFactor=1, multiplier=1\n", - "The following types are not handled by extraction and will be treated as non-electrical types:\n", - " NWELL NWELLT NWELLP DNWELL DIFF TAP LVTN HVTP HVI TUNM POLY POLYP POLYT NPC PSDM NSDM LICON1 LI1 LI1T LI1P MCON MET1 MET1T MET1P VIA1 MET2 MET2T MET2P VIA2 MET3 MET3T MET3P VIA3 MET4 MET4T MET4P VIA4 MET5 MET5T MET5P PAD PADT PADP AREAID TEXT HVTR NCM RPM NSM RDL VHVI LDNTM HVNTM PMM PNP CAP IND PWRES POLYRES DIFFRES DIODE POLYM COREID PWELLT PWELLP CFOMDROP CLI1MADD CNTMADD CP1MADD BOUND RERAM \n", - "Scaled tech values by 2 / 1 to match internal grid scaling\n", - "Input style sky130(): scaleFactor=2, multiplier=2\n", - "The following types are not handled by extraction and will be treated as non-electrical types:\n", - " ubm \n", - "Scaled tech values by 2 / 1 to match internal grid scaling\n", - "Loading sky130A Device Generator Menu ...\n", - "Warning: Calma reading is not undoable! I hope that's OK.\n", - "Library written using GDS-II Release 6.0\n", - "Library name: test\n", - "Reading \"DCL_NMOS_S_62924000_X1_Y71_1733944339\".\n", - "Error while reading cell \"DCL_NMOS_S_62924000_X1_Y71_1733944339\" (byte position 105236): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"DCL_NMOS_S_78879196_X3_Y6_1733944340\".\n", - "Error while reading cell \"DCL_NMOS_S_78879196_X3_Y6_1733944340\" (byte position 125916): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"DCL_PMOS_S_70252776_X3_Y1_1733944341\".\n", - "Error while reading cell \"DCL_PMOS_S_70252776_X3_Y1_1733944341\" (byte position 130340): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"NMOS_4T_21307866_X3_Y3_1733944342\".\n", - "Error while reading cell \"NMOS_4T_21307866_X3_Y3_1733944342\" (byte position 141226): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"NMOS_S_19175688_X1_Y71_1733944343\".\n", - "Error while reading cell \"NMOS_S_19175688_X1_Y71_1733944343\" (byte position 246512): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"NMOS_S_89651636_X3_Y6_1733944344\".\n", - "Error while reading cell \"NMOS_S_89651636_X3_Y6_1733944344\" (byte position 267252): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"PMOS_S_38134054_X1_Y25_1733944345\".\n", - "Error while reading cell \"PMOS_S_38134054_X1_Y25_1733944345\" (byte position 304890): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"CURRENT_MIRROR_OTA_0\".\n", - "Error while reading cell \"CURRENT_MIRROR_OTA_0\" (byte position 364144): Unknown layer/datatype in boundary, layer=235 type=5\n", - "Error while reading cell \"CURRENT_MIRROR_OTA_0\" (byte position 364232): Unknown layer/datatype in boundary, layer=104 type=0\n", - "Processing DCL_NMOS_S_62924000_X1_Y71_1733944339\n", - "Processing DCL_PMOS_S_70252776_X3_Y1_1733944341\n", - "Processing NMOS_S_89651636_X3_Y6_1733944344\n", - "Processing PMOS_S_38134054_X1_Y25_1733944345\n", - "Processing NMOS_4T_21307866_X3_Y3_1733944342\n", - "Processing DCL_NMOS_S_78879196_X3_Y6_1733944340\n", - "Processing NMOS_S_19175688_X1_Y71_1733944343\n", - "Processing CURRENT_MIRROR_OTA_0\n", - "Extracting DCL_NMOS_S_62924000_X1_Y71_1733944339 into DCL_NMOS_S_62924000_X1_Y71_1733944339.ext:\n", - "Extracting DCL_PMOS_S_70252776_X3_Y1_1733944341 into DCL_PMOS_S_70252776_X3_Y1_1733944341.ext:\n", - "Extracting NMOS_S_89651636_X3_Y6_1733944344 into NMOS_S_89651636_X3_Y6_1733944344.ext:\n", - "Extracting PMOS_S_38134054_X1_Y25_1733944345 into PMOS_S_38134054_X1_Y25_1733944345.ext:\n", - "Extracting NMOS_4T_21307866_X3_Y3_1733944342 into NMOS_4T_21307866_X3_Y3_1733944342.ext:\n", - "Extracting DCL_NMOS_S_78879196_X3_Y6_1733944340 into DCL_NMOS_S_78879196_X3_Y6_1733944340.ext:\n", - "Extracting NMOS_S_19175688_X1_Y71_1733944343 into NMOS_S_19175688_X1_Y71_1733944343.ext:\n", - "Extracting CURRENT_MIRROR_OTA_0 into CURRENT_MIRROR_OTA_0.ext:\n", - "exttospice finished.\n", - "Extracted netlist generated at ./CM-OTA-Synthesis/design/gds_-25c/CURRENT_MIRROR_OTA_0.spice\n" - ] - } - ], - "source": [ - "# Run magic with extraction script for -25° C layout\n", - "# Output shows errors - need to investigate, but extraction does run.\n", - "!magic -dnull -noconsole CM-OTA-Synthesis/design/gds_-25c/extract_current_mirror_ota.tcl\n", - "# move results to respective directory\n", - "!mv -f *.ext CM-OTA-Synthesis/design/gds_-25c\n", - "!mv -f *.spice CM-OTA-Synthesis/design/gds_-25c\n", - "print(\"Extracted netlist generated at ./CM-OTA-Synthesis/design/gds_-25c/CURRENT_MIRROR_OTA_0.spice\")" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "d06591c2-bd19-4899-8f6b-ebc89e1293f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Magic 8.3 revision 432 - Compiled on su 24.9.2023 15.09.29 +0300.\n", - "Starting magic under Tcl interpreter\n", - "Using the terminal as the console.\n", - "Using NULL graphics device.\n", - "Processing system .magicrc file\n", - "Loading \"CM-OTA-Synthesis/design/gds_25c/extract_current_mirror_ota.tcl\" from command line.\n", - "Sourcing design .magicrc for technology sky130A ...\n", - "2 Magic internal units = 1 Lambda\n", - "Input style default: scaleFactor=1, multiplier=1\n", - "The following types are not handled by extraction and will be treated as non-electrical types:\n", - " NWELL NWELLT NWELLP DNWELL DIFF TAP LVTN HVTP HVI TUNM POLY POLYP POLYT NPC PSDM NSDM LICON1 LI1 LI1T LI1P MCON MET1 MET1T MET1P VIA1 MET2 MET2T MET2P VIA2 MET3 MET3T MET3P VIA3 MET4 MET4T MET4P VIA4 MET5 MET5T MET5P PAD PADT PADP AREAID TEXT HVTR NCM RPM NSM RDL VHVI LDNTM HVNTM PMM PNP CAP IND PWRES POLYRES DIFFRES DIODE POLYM COREID PWELLT PWELLP CFOMDROP CLI1MADD CNTMADD CP1MADD BOUND RERAM \n", - "Scaled tech values by 2 / 1 to match internal grid scaling\n", - "Input style sky130(): scaleFactor=2, multiplier=2\n", - "The following types are not handled by extraction and will be treated as non-electrical types:\n", - " ubm \n", - "Scaled tech values by 2 / 1 to match internal grid scaling\n", - "Loading sky130A Device Generator Menu ...\n", - "Warning: Calma reading is not undoable! I hope that's OK.\n", - "Library written using GDS-II Release 6.0\n", - "Library name: test\n", - "Reading \"DCL_NMOS_S_54772057_X24_Y4_1733944477\".\n", - "Error while reading cell \"DCL_NMOS_S_54772057_X24_Y4_1733944477\" (byte position 93460): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"DCL_NMOS_S_57935746_X14_Y2_1733944478\".\n", - "Error while reading cell \"DCL_NMOS_S_57935746_X14_Y2_1733944478\" (byte position 122910): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"DCL_PMOS_S_18488141_X5_Y1_1733944479\".\n", - "Error while reading cell \"DCL_PMOS_S_18488141_X5_Y1_1733944479\" (byte position 129510): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"NMOS_4T_3727610_X14_Y1_1733944480\".\n", - "Error while reading cell \"NMOS_4T_3727610_X14_Y1_1733944480\" (byte position 145452): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"NMOS_S_17321006_X14_Y2_1733944481\".\n", - "Error while reading cell \"NMOS_S_17321006_X14_Y2_1733944481\" (byte position 174962): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"NMOS_S_65192303_X24_Y4_1733944482\".\n", - "Error while reading cell \"NMOS_S_65192303_X24_Y4_1733944482\" (byte position 268472): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"PMOS_S_89058261_X15_Y2_1733944483\".\n", - "Error while reading cell \"PMOS_S_89058261_X15_Y2_1733944483\" (byte position 300030): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"CURRENT_MIRROR_OTA_0\".\n", - "Error while reading cell \"CURRENT_MIRROR_OTA_0\" (byte position 337016): Unknown layer/datatype in boundary, layer=235 type=5\n", - "Error while reading cell \"CURRENT_MIRROR_OTA_0\" (byte position 337104): Unknown layer/datatype in boundary, layer=104 type=0\n", - "Processing DCL_NMOS_S_57935746_X14_Y2_1733944478\n", - "Processing DCL_PMOS_S_18488141_X5_Y1_1733944479\n", - "Processing PMOS_S_89058261_X15_Y2_1733944483\n", - "Processing NMOS_4T_3727610_X14_Y1_1733944480\n", - "Processing NMOS_S_17321006_X14_Y2_1733944481\n", - "Processing DCL_NMOS_S_54772057_X24_Y4_1733944477\n", - "Processing NMOS_S_65192303_X24_Y4_1733944482\n", - "Processing CURRENT_MIRROR_OTA_0\n", - "Extracting DCL_NMOS_S_57935746_X14_Y2_1733944478 into DCL_NMOS_S_57935746_X14_Y2_1733944478.ext:\n", - "Extracting DCL_PMOS_S_18488141_X5_Y1_1733944479 into DCL_PMOS_S_18488141_X5_Y1_1733944479.ext:\n", - "Extracting PMOS_S_89058261_X15_Y2_1733944483 into PMOS_S_89058261_X15_Y2_1733944483.ext:\n", - "Extracting NMOS_4T_3727610_X14_Y1_1733944480 into NMOS_4T_3727610_X14_Y1_1733944480.ext:\n", - "Extracting NMOS_S_17321006_X14_Y2_1733944481 into NMOS_S_17321006_X14_Y2_1733944481.ext:\n", - "Extracting DCL_NMOS_S_54772057_X24_Y4_1733944477 into DCL_NMOS_S_54772057_X24_Y4_1733944477.ext:\n", - "Extracting NMOS_S_65192303_X24_Y4_1733944482 into NMOS_S_65192303_X24_Y4_1733944482.ext:\n", - "Extracting CURRENT_MIRROR_OTA_0 into CURRENT_MIRROR_OTA_0.ext:\n", - "exttospice finished.\n", - "\n", - "Extracted netlist generated at ./CM-OTA-Synthesis/design/gds_25c/CURRENT_MIRROR_OTA_0.spice\n" - ] - } - ], - "source": [ - "# Run magic with extraction script for 25° C layout\n", - "# Output shows errors - need to investigate, but extraction does run.\n", - "!magic -dnull -noconsole CM-OTA-Synthesis/design/gds_25c/extract_current_mirror_ota.tcl\n", - "# move results to respective directory\n", - "!mv -f *.ext CM-OTA-Synthesis/design/gds_25c\n", - "!mv -f *.spice CM-OTA-Synthesis/design/gds_25c\n", - "print(\"\")\n", - "print(\"Extracted netlist generated at ./CM-OTA-Synthesis/design/gds_25c/CURRENT_MIRROR_OTA_0.spice\")" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "fb472fc3-a5a1-46c3-b2a2-22b5a0b67f6b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Magic 8.3 revision 432 - Compiled on su 24.9.2023 15.09.29 +0300.\n", - "Starting magic under Tcl interpreter\n", - "Using the terminal as the console.\n", - "Using NULL graphics device.\n", - "Processing system .magicrc file\n", - "Loading \"CM-OTA-Synthesis/design/gds_75c/extract_current_mirror_ota.tcl\" from command line.\n", - "Sourcing design .magicrc for technology sky130A ...\n", - "2 Magic internal units = 1 Lambda\n", - "Input style default: scaleFactor=1, multiplier=1\n", - "The following types are not handled by extraction and will be treated as non-electrical types:\n", - " NWELL NWELLT NWELLP DNWELL DIFF TAP LVTN HVTP HVI TUNM POLY POLYP POLYT NPC PSDM NSDM LICON1 LI1 LI1T LI1P MCON MET1 MET1T MET1P VIA1 MET2 MET2T MET2P VIA2 MET3 MET3T MET3P VIA3 MET4 MET4T MET4P VIA4 MET5 MET5T MET5P PAD PADT PADP AREAID TEXT HVTR NCM RPM NSM RDL VHVI LDNTM HVNTM PMM PNP CAP IND PWRES POLYRES DIFFRES DIODE POLYM COREID PWELLT PWELLP CFOMDROP CLI1MADD CNTMADD CP1MADD BOUND RERAM \n", - "Scaled tech values by 2 / 1 to match internal grid scaling\n", - "Input style sky130(): scaleFactor=2, multiplier=2\n", - "The following types are not handled by extraction and will be treated as non-electrical types:\n", - " ubm \n", - "Scaled tech values by 2 / 1 to match internal grid scaling\n", - "Loading sky130A Device Generator Menu ...\n", - "Warning: Calma reading is not undoable! I hope that's OK.\n", - "Library written using GDS-II Release 6.0\n", - "Library name: test\n", - "Reading \"DCL_NMOS_S_38019457_X10_Y5_1733944612\".\n", - "Error while reading cell \"DCL_NMOS_S_38019457_X10_Y5_1733944612\" (byte position 50132): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"DCL_NMOS_S_55663590_X37_Y4_1733944613\".\n", - "Error while reading cell \"DCL_NMOS_S_55663590_X37_Y4_1733944613\" (byte position 192670): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"DCL_PMOS_S_85143712_X3_Y2_1733944614\".\n", - "Error while reading cell \"DCL_PMOS_S_85143712_X3_Y2_1733944614\" (byte position 200358): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"NMOS_4T_14396096_X25_Y1_1733944615\".\n", - "Error while reading cell \"NMOS_4T_14396096_X25_Y1_1733944615\" (byte position 228268): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"NMOS_S_12565100_X37_Y4_1733944616\".\n", - "Error while reading cell \"NMOS_S_12565100_X37_Y4_1733944616\" (byte position 370866): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"NMOS_S_33976710_X10_Y5_1733944617\".\n", - "Error while reading cell \"NMOS_S_33976710_X10_Y5_1733944617\" (byte position 421048): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"PMOS_S_24460802_X18_Y2_1733944618\".\n", - "Error while reading cell \"PMOS_S_24460802_X18_Y2_1733944618\" (byte position 458558): Unknown layer/datatype in boundary, layer=100 type=5\n", - "Reading \"CURRENT_MIRROR_OTA_0\".\n", - "CIF file read warning: CIF style sky130(): units rescaled by factor of 5 / 1\n", - "Error while reading cell \"CURRENT_MIRROR_OTA_0\" (byte position 503288): Unknown layer/datatype in boundary, layer=235 type=5\n", - "Error while reading cell \"CURRENT_MIRROR_OTA_0\" (byte position 503376): Unknown layer/datatype in boundary, layer=104 type=0\n", - "Processing DCL_NMOS_S_38019457_X10_Y5_1733944612\n", - "Processing DCL_NMOS_S_55663590_X37_Y4_1733944613\n", - "Processing DCL_PMOS_S_85143712_X3_Y2_1733944614\n", - "Processing PMOS_S_24460802_X18_Y2_1733944618\n", - "Processing NMOS_4T_14396096_X25_Y1_1733944615\n", - "Processing NMOS_S_12565100_X37_Y4_1733944616\n", - "Processing NMOS_S_33976710_X10_Y5_1733944617\n", - "Processing CURRENT_MIRROR_OTA_0\n", - "Extracting DCL_NMOS_S_38019457_X10_Y5_1733944612 into DCL_NMOS_S_38019457_X10_Y5_1733944612.ext:\n", - "Extracting DCL_NMOS_S_55663590_X37_Y4_1733944613 into DCL_NMOS_S_55663590_X37_Y4_1733944613.ext:\n", - "Extracting DCL_PMOS_S_85143712_X3_Y2_1733944614 into DCL_PMOS_S_85143712_X3_Y2_1733944614.ext:\n", - "Extracting PMOS_S_24460802_X18_Y2_1733944618 into PMOS_S_24460802_X18_Y2_1733944618.ext:\n", - "Extracting NMOS_4T_14396096_X25_Y1_1733944615 into NMOS_4T_14396096_X25_Y1_1733944615.ext:\n", - "Extracting NMOS_S_12565100_X37_Y4_1733944616 into NMOS_S_12565100_X37_Y4_1733944616.ext:\n", - "Extracting NMOS_S_33976710_X10_Y5_1733944617 into NMOS_S_33976710_X10_Y5_1733944617.ext:\n", - "Extracting CURRENT_MIRROR_OTA_0 into CURRENT_MIRROR_OTA_0.ext:\n", - "exttospice finished.\n", - "\n", - "Extracted netlist generated at ./CM-OTA-Synthesis/design/gds_75c/CURRENT_MIRROR_OTA_0.spice\n" - ] - } - ], - "source": [ - "# Run magic with extraction script for 75° C layout\n", - "# Output shows errors - need to investigate, but extraction does run.\n", - "!magic -dnull -noconsole CM-OTA-Synthesis/design/gds_75c/extract_current_mirror_ota.tcl\n", - "# move results to respective directory\n", - "!mv -f *.ext CM-OTA-Synthesis/design/gds_75c\n", - "!mv -f *.spice CM-OTA-Synthesis/design/gds_75c\n", - "print(\"\")\n", - "print(\"Extracted netlist generated at ./CM-OTA-Synthesis/design/gds_75c/CURRENT_MIRROR_OTA_0.spice\")" - ] - }, - { - "cell_type": "markdown", - "id": "d0acf763-95cd-4e72-a538-55b529c3d9eb", - "metadata": {}, - "source": [ - "**Magic does not add subckt definitions to the netlists generated and must be added.**" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "b6cc42a0-730d-4364-8900-7e84aa473fad", - "metadata": {}, - "outputs": [], - "source": [ - "# Generated extractions do contain subckt definitons\n", - "def make_subckt_from_extraction(input_file_path, output_file_path):\n", - " subckt_declaration = \".subckt cm_ota_extracted ID VOUT VINN VINP VSS VDD\\n\"\n", - " subckt_end = \".ends\\n\"\n", - " try:\n", - " with open(input_file_path, 'r') as input_file:\n", - " lines = input_file.readlines()\n", - " \n", - " # Remove all occurrences of string \" **FLOATING\"\n", - " modified_lines = [line.replace(\" **FLOATING\", \"\") for line in lines]\n", - " \n", - " # Add subcircuit declaration at the top and \".ends\" at the bottom\n", - " modified_lines.insert(0, subckt_declaration) # Add at the beginning\n", - " modified_lines.append(subckt_end) # Add at the end\n", - "\n", - " # Write the modified content to the output file\n", - " with open(output_file_path, 'w') as output_file:\n", - " output_file.writelines(modified_lines)\n", - " \n", - " print(f\"Extracted netlist created and written to {output_file_path}\")\n", - " \n", - " except Exception as e:\n", - " print(f\"An error occurred: {e}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "fe9e9658-4a12-4777-887b-1e06b5c51a94", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracted netlist created and written to CM-OTA-Synthesis/design/simulation/spice_-25c/cm_ota_extracted.sp\n", - "Extracted netlist created and written to CM-OTA-Synthesis/design/simulation/spice_25c/cm_ota_extracted.sp\n", - "Extracted netlist created and written to CM-OTA-Synthesis/design/simulation/spice_75c/cm_ota_extracted.sp\n" - ] - } - ], - "source": [ - "make_subckt_from_extraction(\"CM-OTA-Synthesis/design/gds_-25c/CURRENT_MIRROR_OTA_0.spice\", \"CM-OTA-Synthesis/design/simulation/spice_-25c/cm_ota_extracted.sp\")\n", - "make_subckt_from_extraction(\"CM-OTA-Synthesis/design/gds_25c/CURRENT_MIRROR_OTA_0.spice\", \"CM-OTA-Synthesis/design/simulation/spice_25c/cm_ota_extracted.sp\")\n", - "make_subckt_from_extraction(\"CM-OTA-Synthesis/design/gds_75c/CURRENT_MIRROR_OTA_0.spice\", \"CM-OTA-Synthesis/design/simulation/spice_75c/cm_ota_extracted.sp\")" - ] - }, - { - "cell_type": "markdown", - "id": "d90ae6a4-bddb-4164-ae9e-c9cf49298a8b", - "metadata": {}, - "source": [ - "## **Run Simulations using ngspice**\n", - "**Run DC and AC simulations using ngspice**" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "9fe75e3f-b519-4412-8a0c-0154aad5cee9", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "# Function for pre-processing spice testbench/netlist\n", - "# ngspice cannot handle $PDK_ROOT variable\n", - "def replace_pdk_root(input_file_path, output_file_path):\n", - " # Get the value of the PDK_ROOT environment variable\n", - " pdk_root_value = os.getenv('PDK_ROOT')\n", - " if not pdk_root_value:\n", - " raise EnvironmentError(\"Environment variable PDK_ROOT is not set.\")\n", - " try:\n", - " # Read the input file\n", - " with open(input_file_path, 'r') as input_file:\n", - " content = input_file.read()\n", - " # Replace all occurrences of $PDK_ROOT with the environment variable value\n", - " modified_content = content.replace('$PDK_ROOT', pdk_root_value)\n", - " # Write the modified content to the output file\n", - " with open(output_file_path, 'w') as output_file:\n", - " output_file.write(modified_content)\n", - " print(f\"Successfully replaced $PDK_ROOT and wrote to {output_file_path}\")\n", - " except Exception as e:\n", - " print(f\"An error occurred: {e}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "1c997ad4-1c00-4906-aea2-e6861da95be5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully replaced $PDK_ROOT and wrote to tb_cm_ota_-25c.sp\n", - "\n", - "No compatibility mode selected!\n", - "\n", - "\n", - "Circuit: \n", - "\n", - "^C\n", - "\n", - "\n", - "mv: cannot move 'ac_output_ext.txt' to 'design/simulations/spice_-25c': No such file or directory\n", - "mv: cannot move 'ac_output.txt' to 'design/simulations/spice_-25c': No such file or directory\n" - ] - } - ], - "source": [ - "replace_pdk_root(\"CM-OTA-Synthesis/design/simulation/spice_-25c/tb_cm_ota.sp\", \"tb_cm_ota_-25c.sp\")\n", - "!ngspice -b tb_cm_ota_-25c.sp\n", - "!rm tb_cm_ota_-25c.sp\n", - "!mv -f ac_output_ext.txt CM-OTA-Synthesis/design/simulations/spice_-25c\n", - "!mv -f ac_output.txt CM-OTA-Synthesis/design/simulations/spice_-25c" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "dd8d4523-180a-4429-8d49-ab0f78d003ce", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully replaced $PDK_ROOT and wrote to tb_cm_ota_25c.sp\n", - "^C\n", - "\n", - "\n", - "mv: cannot move 'ac_output_ext.txt' to 'design/simulations/spice_25c': No such file or directory\n", - "mv: cannot move 'ac_output.txt' to 'design/simulations/spice_25c': No such file or directory\n" - ] - } - ], - "source": [ - "replace_pdk_root(\"CM-OTA-Synthesis/design/simulation/spice_25c/tb_cm_ota.sp\", \"tb_cm_ota_25c.sp\")\n", - "!ngspice -b tb_cm_ota_25c.sp\n", - "!rm tb_cm_ota_25c.sp\n", - "!mv -f ac_output_ext.txt CM-OTA-Synthesis/design/simulations/spice_25c\n", - "!mv -f ac_output.txt CM-OTA-Synthesis/design/simulations/spice_25c" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "e3bdb528-bcbf-4d5f-a62d-74ab748c2f9f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully replaced $PDK_ROOT and wrote to tb_cm_ota_75c.sp\n", - "^C\n", - "\n", - "\n", - "mv: cannot move 'ac_output_ext.txt' to 'design/simulations/spice_75c': No such file or directory\n", - "mv: cannot move 'ac_output.txt' to 'design/simulations/spice_75c': No such file or directory\n" - ] - } - ], - "source": [ - "replace_pdk_root(\"CM-OTA-Synthesis/design/simulation/spice_75c/tb_cm_ota.sp\", \"tb_cm_ota_75c.sp\")\n", - "!ngspice -b tb_cm_ota_75c.sp\n", - "!rm tb_cm_ota_75c.sp\n", - "!mv -f ac_output_ext.txt CM-OTA-Synthesis/design/simulations/spice_75c\n", - "!mv -f ac_output.txt CM-OTA-Synthesis/design/simulations/spice_75c" - ] - }, - { - "cell_type": "markdown", - "id": "5e3dcc38-be70-4fe3-ab43-f02f4f3545e1", - "metadata": {}, - "source": [ - "---\n", - "## **Ideal SPICE and Post Layout Simulation Results and Comparsion**" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "df2fc2ee-cafc-4432-aa72-3bcfd6e63d86", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DC Gain -25° C ideal: 35.1779245dB\n", - "Gain Bandwidth Product -25° C ideal: 95.49925859999999 MHz\n", - "Phase Margin -25° C ideal: 73.60160509095546°\n", - "\n", - "DC Gain -25° C Post Layout: 35.5316726dB\n", - "Gain Bandwidth Product -25° C Post Layout: 104.712855 MHz\n", - "Phase Margin -25° C Post Layout: 61.77066088578317°\n", - "\n", - "DC Gain 25° C ideal: 38.8797188dB\n", - "Gain Bandwidth Product 25° C ideal: 102.329299 MHz\n", - "Phase Margin 25° C ideal: 69.35725807646786°\n", - "\n", - "DC Gain 25° C Post Layout: 35.3649017dB\n", - "Gain Bandwidth Product 25° C Post Layout: 93.32543009999999 MHz\n", - "Phase Margin 25° C Post Layout: 63.70963314338616°\n", - "\n", - "DC Gain 75° C ideal: 37.3950306dB\n", - "Gain Bandwidth Product 75° C ideal: 100.0 MHz\n", - "Phase Margin 75° C ideal: 65.00558346188076°\n", - "\n", - "DC Gain 75° C Post Layout: 34.8598709dB\n", - "Gain Bandwidth Product 75° C Post Layout: 87.096359 MHz\n", - "Phase Margin 75° C Post Layout: 64.20047691915002°\n", - "\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_ac_results(frequency, magnitude_db, phase_deg, line_style, line_color, label_suffix=\"\", fig=None, ax1=None, ax2=None):\n", - " # Define font path and size within the function\n", - " arial_font = \"src/fonts/ArialNarrow/arialnarrow_bold.ttf\"\n", - " font_size = 8 # Adjust font size for labels, ticks, etc.\n", - " font_properties = FontProperties(fname=arial_font, size=font_size)\n", - " ideal = False\n", - " # Create the figure and axes if not provided (for the first call)\n", - " if fig is None or ax1 is None or ax2 is None:\n", - " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(3.5, 2.8 * 2), dpi=300)\n", - " #fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(7, 11), dpi=350)\n", - " ideal = True\n", - "\n", - " # Find the relevant points\n", - " dc_gain, pole_1, pole_2, unity_gain_freq = find_relevant_points(frequency, magnitude_db, phase_deg)\n", - " ax1.plot(frequency, magnitude_db, linestyle=line_style, color=line_color, label=f'{label_suffix}', linewidth=1)\n", - "\n", - " # Annotate the DC Gain at 100 Hz with a bubble\n", - " magnitude_100Hz = np.interp(100, frequency, magnitude_db)\n", - " # Plot phase in degrees (Ideal or Extracted)\n", - "\n", - " ax2.plot(frequency, phase_deg, linestyle=line_style, color=line_color, label=f'{label_suffix}', linewidth=1)\n", - " # Annotate the phase margin at the unity gain frequency\n", - " if unity_gain_freq is not None:\n", - " phase_at_unity = np.interp(unity_gain_freq, frequency, phase_deg) # Phase at unity gain frequency\n", - " phase_margin = 180 + phase_at_unity # Phase margin calculation\n", - " #ax2.plot(unity_gain_freq, phase_at_unity, 'ko', markersize=6) # Mark point at unity gain frequency\n", - " #ax2.annotate(f'{label_suffix} Phase: {phase_at_unity:.1f}°\\nPhase Margin: {phase_margin:.1f}°',\n", - " # xy=(unity_gain_freq, phase_at_unity),\n", - " # xytext=(unity_gain_freq * 1.2, phase_at_unity - 10),\n", - " # fontsize=font_size, fontproperties=font_properties, ha='left',\n", - " # bbox=dict(boxstyle=\"round,pad=0.3\", edgecolor=\"black\", facecolor=\"lightyellow\"))\n", - "\n", - " return fig, ax1, ax2, dc_gain, unity_gain_freq, phase_margin\n", - "\n", - "# Function to read the AC simulation data\n", - "def read_ac_simulation_data(filename):\n", - " # Read the data file\n", - " data = np.loadtxt(filename, skiprows=1) # Skip the header row\n", - "\n", - " # The file format is expected to be: frequency vdb(2) vp(2)\n", - " frequency = data[:, 0]\n", - " magnitude_db = data[:, 4]\n", - " phase_deg = (180/math.pi)*data[:, 6]\n", - "\n", - " return frequency, magnitude_db, phase_deg\n", - "\n", - "# Function to find relevant points (DC Gain, Pole 1, Pole 2, Unity Gain Frequency)\n", - "def find_relevant_points(frequency, magnitude_db, phase_deg):\n", - " # DC Gain: the magnitude at the lowest frequency (0 Hz or close to it)\n", - " dc_gain = magnitude_db[0]\n", - " # Unity Gain Frequency: the frequency where magnitude crosses 0 dB\n", - " unity_gain_freq = None\n", - " for i in range(len(magnitude_db) - 1):\n", - " if magnitude_db[i] > 0 and magnitude_db[i + 1] < 0:\n", - " unity_gain_freq = frequency[i]\n", - " break\n", - " # Poles: Find where phase crosses -45 degrees (Pole 1) and -135 degrees (Pole 2)\n", - " pole_1 = None\n", - " pole_2 = None\n", - " for i in range(1, len(phase_deg)):\n", - " if phase_deg[i] <= -45 and pole_1 is None:\n", - " pole_1 = frequency[i] # Approximate the frequency where phase crosses -45 degrees\n", - " elif phase_deg[i] <= -135 and pole_2 is None:\n", - " pole_2 = frequency[i] # Approximate the frequency where phase crosses -135 degrees\n", - " break\n", - " return dc_gain, pole_1, pole_2, unity_gain_freq\n", - "\n", - "\n", - "# Function to calculate percentage difference between two values\n", - "def percentage_difference_two_values(value1, value2):\n", - " return 100 * (value2 - value1) / value1\n", - "\n", - "def percentage_difference(values):\n", - " if not values:\n", - " return 0\n", - " # Calculate the mean of the values\n", - " mean_value = sum(values) / len(values)\n", - " # Calculate the percentage difference for each value\n", - " percentage_differences = [(value - mean_value) / mean_value * 100 for value in values]\n", - " # Return the average of the percentage differences\n", - " return sum(percentage_differences) / len(percentage_differences)\n", - "\n", - "# Main function to execute the script and handle comparison\n", - "#def plot_spice_results():\n", - "arial_font = \"CM-OTA-Synthesis/src/fonts/ArialNarrow/arialnarrow_bold.ttf\"\n", - "font_size = 8\n", - "arial_bold = FontProperties(fname=arial_font, size=font_size)\n", - "font_properties = arial_bold\n", - "# Read ideal simulation data\n", - "\n", - "dc_gains = []\n", - "unity_gains = []\n", - "phase_margins = []\n", - "\n", - " # Read extracted simulation data\n", - "file_neg25_sch = 'CM-OTA-Synthesis/design/simulation/golden_sims/neg_25/ac_output.txt'\n", - "freq_neg25_sch, mag_neg25_sch, phase_neg25_sch = read_ac_simulation_data(file_neg25_sch)\n", - "# Plot extracted results and pass in the figure and axes from the ideal plot\n", - "line_style = \"-\"\n", - "line_color=\"blue\"\n", - "\n", - "fig, ax1, ax2, dc_gain_ext, unity_gain_ext, phase_margin_ext = plot_ac_results(\n", - " freq_neg25_sch, mag_neg25_sch, phase_neg25_sch, line_style, line_color, label_suffix=\"Schematic -25° C\"\n", - ")\n", - "dc_gains.append(dc_gain_ext)\n", - "unity_gains.append(unity_gain_ext)\n", - "phase_margins.append(phase_margin_ext)\n", - "print(\"DC Gain -25° C ideal: \" + str(dc_gain_ext) + \"dB\")\n", - "print(\"Gain Bandwidth Product -25° C ideal: \" + str(unity_gain_ext/1e6) + \" MHz\")\n", - "print(\"Phase Margin -25° C ideal: \" + str(phase_margin_ext) + \"°\")\n", - "print(\"\")\n", - "\n", - "file_neg25_ext = 'CM-OTA-Synthesis/design/simulation/golden_sims/neg_25/ac_output_ext.txt'\n", - "freq_neg25_ext, mag_neg25_ext, phase_neg25_ext = read_ac_simulation_data(file_neg25_ext)\n", - "# Plot extracted results and pass in the figure and axes from the ideal plot\n", - "line_style = \"--\"\n", - "line_color=\"blue\"\n", - "fig, ax1, ax2, dc_gain_ext, unity_gain_ext, phase_margin_ext = plot_ac_results(\n", - " freq_neg25_ext, mag_neg25_ext, phase_neg25_ext, line_style, line_color, label_suffix=\"Post Layout -25° C\", fig=fig, ax1=ax1, ax2=ax2\n", - ")\n", - "dc_gains.append(dc_gain_ext)\n", - "unity_gains.append(unity_gain_ext)\n", - "phase_margins.append(phase_margin_ext)\n", - "\n", - "print(\"DC Gain -25° C Post Layout: \" + str(dc_gain_ext) + \"dB\")\n", - "print(\"Gain Bandwidth Product -25° C Post Layout: \" + str(unity_gain_ext/(1e6)) + \" MHz\")\n", - "print(\"Phase Margin -25° C Post Layout: \" + str(phase_margin_ext) + \"°\")\n", - "print(\"\")\n", - "\n", - "\n", - "filename = 'CM-OTA-Synthesis/design/simulation/golden_sims/25/ac_output.txt'\n", - "frequency, magnitude_db, phase_deg = read_ac_simulation_data(filename)\n", - "line_style = '-'\n", - "line_color = \"green\"\n", - "# Plot ideal results\n", - "fig, ax1, ax2, dc_gain_ideal, unity_gain_ideal, phase_margin_ideal = plot_ac_results(\n", - " frequency, magnitude_db, phase_deg, line_style, line_color, label_suffix=\"Schematic 25° C\", fig=fig, ax1=ax1, ax2=ax2\n", - ")\n", - "dc_gains.append(dc_gain_ideal)\n", - "unity_gains.append(unity_gain_ideal)\n", - "phase_margins.append(phase_margin_ideal)\n", - "\n", - "print(\"DC Gain 25° C ideal: \" + str(dc_gain_ideal) + \"dB\")\n", - "print(\"Gain Bandwidth Product 25° C ideal: \" + str(unity_gain_ideal/(1e6)) + \" MHz\")\n", - "print(\"Phase Margin 25° C ideal: \" + str(phase_margin_ideal) + \"°\")\n", - "print(\"\")\n", - "\n", - "file_25_ext = 'CM-OTA-Synthesis/design/simulation/golden_sims/25/ac_output_ext.txt'\n", - "freq_25_ext, mag_25_ext, phase_25_ext = read_ac_simulation_data(file_25_ext)\n", - "\n", - "\n", - "# Plot extracted results and pass in the figure and axes from the ideal plot\n", - "line_style = \"--\"\n", - "line_color=\"green\"\n", - "fig, ax1, ax2, dc_gain_ext, unity_gain_ext, phase_margin_ext = plot_ac_results(\n", - " freq_25_ext, mag_25_ext, phase_25_ext, line_style, line_color, label_suffix=\"Post Layout 25° C\", fig=fig, ax1=ax1, ax2=ax2\n", - ")\n", - "dc_gains.append(dc_gain_ext)\n", - "unity_gains.append(unity_gain_ext)\n", - "phase_margins.append(phase_margin_ext)\n", - "\n", - "print(\"DC Gain 25° C Post Layout: \" + str(dc_gain_ext) + \"dB\")\n", - "print(\"Gain Bandwidth Product 25° C Post Layout: \" + str(unity_gain_ext/1e6) + \" MHz\")\n", - "print(\"Phase Margin 25° C Post Layout: \" + str(phase_margin_ext) + \"°\")\n", - "print(\"\")\n", - "\n", - "\n", - "\n", - "file_75_ext = 'CM-OTA-Synthesis/design/simulation/golden_sims/75/ac_output.txt'\n", - "freq_75_ext, mag_75_ext, phase_75_ext = read_ac_simulation_data(file_75_ext)\n", - "# Plot extracted results and pass in the figure and axes from the ideal plot\n", - "line_style = \"-\"\n", - "line_color=\"red\"\n", - "fig, ax1, ax2, dc_gain_ext, unity_gain_ext, phase_margin_ext = plot_ac_results(\n", - " freq_75_ext, mag_75_ext, phase_75_ext, line_style, line_color, label_suffix=\"Schematic 75° C\", fig=fig, ax1=ax1, ax2=ax2\n", - ")\n", - "dc_gains.append(dc_gain_ext)\n", - "unity_gains.append(unity_gain_ext)\n", - "phase_margins.append(phase_margin_ext)\n", - "\n", - "print(\"DC Gain 75° C ideal: \" + str(dc_gain_ext) + \"dB\")\n", - "print(\"Gain Bandwidth Product 75° C ideal: \" + str(unity_gain_ext/1e6) + \" MHz\")\n", - "print(\"Phase Margin 75° C ideal: \" + str(phase_margin_ext) + \"°\")\n", - "print(\"\")\n", - "\n", - "# Calculate percentage differences and annotate them\n", - "dc_gain_diff = percentage_difference(dc_gains)\n", - "unity_gain_diff = percentage_difference(unity_gains)\n", - "phase_margin_diff = percentage_difference(phase_margins)\n", - "\n", - "file_75_ext = 'CM-OTA-Synthesis/design/simulation/golden_sims/75/ac_output_ext.txt'\n", - "freq_75_ext, mag_75_ext, phase_75_ext = read_ac_simulation_data(file_75_ext)\n", - "# Plot extracted results and pass in the figure and axes from the ideal plot\n", - "line_style = \"--\"\n", - "line_color=\"red\"\n", - "fig, ax1, ax2, dc_gain_ext, unity_gain_ext, phase_margin_ext = plot_ac_results(\n", - " freq_75_ext, mag_75_ext, phase_75_ext, line_style, line_color, label_suffix=\"Post Layout 75° C\", fig=fig, ax1=ax1, ax2=ax2\n", - ")\n", - "dc_gains.append(dc_gain_ext)\n", - "unity_gains.append(unity_gain_ext)\n", - "phase_margins.append(phase_margin_ext)\n", - "\n", - "print(\"DC Gain 75° C Post Layout: \" + str(dc_gain_ext) + \"dB\")\n", - "print(\"Gain Bandwidth Product 75° C Post Layout: \" + str(unity_gain_ext/1e6) + \" MHz\")\n", - "print(\"Phase Margin 75° C Post Layout: \" + str(phase_margin_ext) + \"°\")\n", - "print(\"\")\n", - "\n", - "# Calculate percentage differences and annotate them\n", - "dc_gain_diff = percentage_difference(dc_gains)\n", - "unity_gain_diff = percentage_difference(unity_gains)\n", - "phase_margin_diff = percentage_difference(phase_margins)\n", - "\n", - "for label in ax1.get_xticklabels():\n", - " label.set_fontproperties(arial_bold)\n", - "for label in ax1.get_yticklabels():\n", - " label.set_fontproperties(arial_bold)\n", - "minor_size = font_size - 2\n", - "ax1.set_xscale('log')\n", - "ax1.set_ylabel('Magnitude [dB]', fontproperties=font_properties)\n", - "ax1.grid(which='both', linestyle='--', linewidth=0.6)\n", - "ax1.xaxis.set_major_locator(LogLocator(base=10.0, numticks=12))\n", - "ax1.xaxis.set_minor_locator(LogLocator(base=10.0, subs=np.arange(2, 10) * 0.1, numticks=10))\n", - "ax1.set_xlim(10, 1e11)\n", - "\n", - "ax1.tick_params(labelsize=font_size)\n", - "ax2.set_xscale('log')\n", - "ax2.set_ylabel('Phase [°]', fontproperties=font_properties)\n", - "ax2.grid(which='both', linestyle='--', linewidth=0.6)\n", - "ax2.xaxis.set_major_locator(LogLocator(base=10.0, numticks=12))\n", - "\n", - "ax2.xaxis.set_minor_locator(LogLocator(base=10.0, subs=np.arange(2, 10) * 0.1, numticks=10))\n", - "ax2.set_xlim(10, 1e11)\n", - "ax2.tick_params(labelsize=font_size)\n", - "\n", - "\n", - "for label in ax2.get_xticklabels():\n", - " label.set_fontproperties(arial_bold)\n", - "for label in ax2.get_yticklabels():\n", - " label.set_fontproperties(arial_bold)\n", - "minor_size = font_size - 2\n", - "ax2.tick_params(axis='both', which='major', labelsize=font_size)\n", - "\n", - "\n", - "\n", - "#ax1.tick_params(axis='both', which='minor', labelsize=minor_size)\n", - "ax1.legend(prop=arial_bold)\n", - "ax2.legend(prop=arial_bold)\n", - "plt.grid(True)\n", - "plt.savefig(\"images/ac_simulation_plot.svg\", format=\"svg\")\n", - "plt.savefig(\"images/ac_simulation_plot.png\", format=\"png\", dpi=300)\n", - "#plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "26895bb1-4fe6-4487-a69d-161bb929a9c6", - "metadata": {}, - "source": [ - "## **Bode Plots**" - ] - }, - { - "cell_type": "markdown", - "id": "31a7ea59-222b-48e2-bfbb-4b8133e1564f", - "metadata": {}, - "source": [ - "\"png\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8afaf5b-1615-4763-883e-8e596a71f4be", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/LICENSE b/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/LICENSE index ada0a48a..2e67a24e 100644 --- a/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/LICENSE +++ b/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/LICENSE @@ -187,7 +187,7 @@ same "printed page" as the copyright notice for easier identification within third-party archives. - Copyright [2024] [Alec Stefan Adair] + Copyright 2024 Alec Stefan Adair Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/current_mirror_ota_optimization.ipynb b/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/current_mirror_ota_optimization.ipynb index d3611091..70667ea9 100644 --- a/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/current_mirror_ota_optimization.ipynb +++ b/ISSCC25/submitted_notebooks/current_mirror_ota_optimization/current_mirror_ota_optimization.ipynb @@ -15,9 +15,9 @@ "\n", "Please feel free to reach out!\n", "\n", - "**Work Licensed Under GPL 3.0**\n", + "**Work Licensed Under Apache 2.0**\n", "\n", - "Details of GPL 3.0 can be read in the LICENSE file of top level directory\n", + "Details of the Apache 2.0 license can be read in the LICENSE file of top level directory\n", "\n", "---\n", "\n", @@ -135,7 +135,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (21.3.1)\r\n" + "Requirement already satisfied: pip in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (24.3.1)\n" ] } ], @@ -342,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "629b341d-6f17-4ced-b20c-5a4c9d9de6b3", "metadata": {}, "outputs": [ @@ -350,72 +350,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: colorlog in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (6.9.0)\n", - "\u001b[31mERROR: Could not find a version that satisfies the requirement pydantic==1.10.18 (from versions: 0.0.1, 0.0.2, 0.0.3, 0.0.4, 0.0.5, 0.0.6, 0.0.7, 0.0.8, 0.1, 0.2, 0.2.1, 0.3, 0.4, 0.5, 0.6, 0.6.1, 0.6.2, 0.6.3, 0.6.4, 0.7, 0.7.1, 0.8, 0.9, 0.9.1, 0.10, 0.11, 0.11.1, 0.11.2, 0.12, 0.12.1, 0.13, 0.13.1, 0.14, 0.15, 0.16, 0.16.1, 0.17, 0.18, 0.18.1, 0.18.2, 0.19, 0.20a1, 0.20, 0.20.1, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27a1, 0.27, 0.28, 0.29, 0.30, 0.30.1, 0.31, 0.31.1, 0.32, 0.32.1, 0.32.2, 1.0b1, 1.0b2, 1.0, 1.1, 1.1.1, 1.2, 1.3, 1.4, 1.5, 1.5.1, 1.6, 1.6.1, 1.6.2, 1.7, 1.7.1, 1.7.2, 1.7.3, 1.7.4, 1.8, 1.8.1, 1.8.2, 1.9.0a1, 1.9.0a2, 1.9.0, 1.9.1, 1.9.2)\u001b[0m\n", - "\u001b[31mERROR: No matching distribution found for pydantic==1.10.18\u001b[0m\n", - "Requirement already satisfied: z3 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (0.2.0)\n", - "Requirement already satisfied: boto in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from z3) (2.49.0)\n", - "Requirement already satisfied: networkx in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (2.5.1)\n", - "Requirement already satisfied: decorator<5,>=4.3 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from networkx) (4.4.2)\n", - "Requirement already satisfied: flatdict in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (4.0.1)\n", - "Requirement already satisfied: python-gdsii in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (0.2.1)\n", - "Collecting gdspy\n", - " Using cached gdspy-1.6.13.zip (157 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: numpy in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from gdspy) (1.19.5)\n", - "Using legacy 'setup.py install' for gdspy, since package 'wheel' is not installed.\n", - "Installing collected packages: gdspy\n", - " Running setup.py install for gdspy ... \u001b[?25lerror\n", - "\u001b[31m ERROR: Command errored out with exit status 1:\n", - " command: /pri/ala1/Documents/CAD_Custom_Scripts/venv2/bin/python3 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-a35p8b6w/gdspy_c5ac6b8845e441659cb4ec9e32d196cb/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-a35p8b6w/gdspy_c5ac6b8845e441659cb4ec9e32d196cb/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-k5wg4mlf/install-record.txt --single-version-externally-managed --compile --install-headers /pri/ala1/Documents/CAD_Custom_Scripts/venv2/include/site/python3.6/gdspy\n", - " cwd: /tmp/pip-install-a35p8b6w/gdspy_c5ac6b8845e441659cb4ec9e32d196cb/\n", - " Complete output (40 lines):\n", - " running install\n", - " running build\n", - " running build_py\n", - " creating build\n", - " creating build/lib.linux-x86_64-3.6\n", - " creating build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/hobby.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/library.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/path.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/viewer.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/curve.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/__init__.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/label.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/operation.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/polygon.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " copying gdspy/gdsiiformat.py -> build/lib.linux-x86_64-3.6/gdspy\n", - " creating build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/09.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/03.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/up.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/01.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/00.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/07.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/04.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/05.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/08.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/down.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/outline.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/06.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " copying gdspy/data/02.xbm -> build/lib.linux-x86_64-3.6/gdspy/data\n", - " running build_ext\n", - " building 'gdspy.clipper' extension\n", - " creating build/temp.linux-x86_64-3.6\n", - " creating build/temp.linux-x86_64-3.6/gdspy\n", - " gcc -pthread -Wno-unused-result -Wsign-compare -DDYNAMIC_ANNOTATIONS_ENABLED=1 -DNDEBUG -O2 -g -pipe -Wall -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -fexceptions -fstack-protector-strong -grecord-gcc-switches -m64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -D_GNU_SOURCE -fPIC -fwrapv -O2 -g -pipe -Wall -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -fexceptions -fstack-protector-strong -grecord-gcc-switches -m64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -D_GNU_SOURCE -fPIC -fwrapv -O2 -g -pipe -Wall -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -fexceptions -fstack-protector-strong -grecord-gcc-switches -m64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -D_GNU_SOURCE -fPIC -fwrapv -fPIC -I/pri/ala1/Documents/CAD_Custom_Scripts/venv2/include -I/usr/include/python3.6m -c gdspy/clipper.cpp -o build/temp.linux-x86_64-3.6/gdspy/clipper.o\n", - " gdspy/clipper.cpp:43:10: fatal error: Python.h: No such file or directory\n", - " #include \n", - " ^~~~~~~~~~\n", - " compilation terminated.\n", - " error: command 'gcc' failed with exit status 1\n", - " ----------------------------------------\u001b[0m\n", - "\u001b[31mERROR: Command errored out with exit status 1: /pri/ala1/Documents/CAD_Custom_Scripts/venv2/bin/python3 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-a35p8b6w/gdspy_c5ac6b8845e441659cb4ec9e32d196cb/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-a35p8b6w/gdspy_c5ac6b8845e441659cb4ec9e32d196cb/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-k5wg4mlf/install-record.txt --single-version-externally-managed --compile --install-headers /pri/ala1/Documents/CAD_Custom_Scripts/venv2/include/site/python3.6/gdspy Check the logs for full command output.\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: plotly in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (5.18.0)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from plotly) (8.2.2)\n", - "Requirement already satisfied: packaging in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from plotly) (21.3)\n", - "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /pri/ala1/Documents/CAD_Custom_Scripts/venv2/lib/python3.6/site-packages (from packaging->plotly) (3.1.4)\n" + "Requirement already satisfied: colorlog in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (6.9.0)\n", + "Requirement already satisfied: pydantic==1.10.18 in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (1.10.18)\n", + "Requirement already satisfied: typing-extensions>=4.2.0 in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (from pydantic==1.10.18) (4.12.2)\n", + "Requirement already satisfied: z3 in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (0.2.0)\n", + "Requirement already satisfied: boto in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (from z3) (2.49.0)\n", + "Requirement already satisfied: networkx in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (3.4.2)\n", + "Requirement already satisfied: flatdict in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (4.0.1)\n", + "Requirement already satisfied: python-gdsii in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (0.2.1)\n", + "Requirement already satisfied: gdspy in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (1.6.13)\n", + "Requirement already satisfied: numpy in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (from gdspy) (2.2.0)\n", + "Requirement already satisfied: plotly in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (5.24.1)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (from plotly) (9.0.0)\n", + "Requirement already satisfied: packaging in /home/adair/Documents/CAD/sscs_venv/lib/python3.10/site-packages (from plotly) (24.2)\n" ] } ], @@ -443,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "id": "c6aff259", "metadata": {}, "outputs": [ @@ -456,9 +403,9 @@ "remote: Counting objects: 100% (723/723), done.\u001b[K\n", "remote: Compressing objects: 100% (290/290), done.\u001b[K\n", "remote: Total 723 (delta 430), reused 717 (delta 427), pack-reused 0 (from 0)\u001b[K\n", - "Receiving objects: 100% (723/723), 12.72 MiB | 27.90 MiB/s, done.\n", + "Receiving objects: 100% (723/723), 12.72 MiB | 6.40 MiB/s, done.\n", "Resolving deltas: 100% (430/430), done.\n", - "Checking out files: 100% (497/497), done.\n" + "Updating files: 100% (497/497), done.\n" ] } ], @@ -477,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "583e3a55-19f4-4961-83ed-112f985b14b9", "metadata": {}, "outputs": [], @@ -1000,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 5, "id": "93e35c0c-c67e-41a7-8729-120a07a1e172", "metadata": {}, "outputs": [ @@ -1008,7 +955,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Lookup Tables Stored in ./CM-OTA-Synthesis/characterization\n", + "Lookup Tables Stored in CM-OTA-Synthesis/characterization\n", "Lookup Table CID Objects created\n" ] } @@ -1105,7 +1052,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 6, "id": "e8b126c3-b502-48c3-8fde-4150bac3fe33", "metadata": {}, "outputs": [], @@ -1218,7 +1165,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 7, "id": "6b6d090d-e41a-4f1b-87e0-f18bec1b78b7", "metadata": {}, "outputs": [], @@ -1239,7 +1186,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 8, "id": "963e106e-a271-42cb-8b3e-716cf55020f0", "metadata": {}, "outputs": [], @@ -2928,7 +2875,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 9, "id": "df2fc2ee-cafc-4432-aa72-3bcfd6e63d86", "metadata": {}, "outputs": [ @@ -3257,7 +3204,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -3271,7 +3218,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - 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+ @@ -110,11 +110,11 @@ L 70.56 48.384 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -131,11 +131,11 @@ L 90.09 48.384 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -152,11 +152,11 @@ L 109.62 48.384 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -173,11 +173,11 @@ L 129.15 48.384 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -194,11 +194,11 @@ L 148.68 48.384 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -215,11 +215,11 @@ L 168.21 48.384 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -236,11 +236,11 @@ L 187.74 48.384 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -257,11 +257,11 @@ L 207.27 48.384 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -280,16 +280,16 @@ L 226.8 48.384 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - - + @@ -300,11 +300,11 @@ L -3.5 0 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -315,11 +315,11 @@ L 226.8 147.711969 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -330,11 +330,11 @@ L 226.8 124.153715 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -345,11 +345,11 @@ L 226.8 100.59546 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -360,11 +360,11 @@ L 226.8 77.037206 +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -416,7 +416,7 @@ L 220.7457 173.678148 L 225.0423 173.682677 L 226.9953 173.617554 L 226.9953 173.617554 -" clip-path="url(#p142f88d15b)" style="fill: none; stroke: #0000ff; stroke-linecap: square"/> +" clip-path="url(#p6e693020d8)" style="fill: none; stroke: #0000ff; stroke-linecap: square"/> +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 3.7,1.6; stroke-dashoffset: 0; stroke: #0000ff"/> +" clip-path="url(#p6e693020d8)" style="fill: none; stroke: #008000; stroke-linecap: square"/> +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 3.7,1.6; stroke-dashoffset: 0; stroke: #008000"/> +" clip-path="url(#p6e693020d8)" style="fill: none; stroke: #ff0000; stroke-linecap: square"/> +" clip-path="url(#p6e693020d8)" style="fill: none; stroke-dasharray: 3.7,1.6; stroke-dashoffset: 0; stroke: #ff0000"/> +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -754,11 +754,11 @@ L 31.5 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -775,11 +775,11 @@ L 51.03 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -796,11 +796,11 @@ L 70.56 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -817,11 +817,11 @@ L 90.09 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -838,11 +838,11 @@ L 109.62 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -859,11 +859,11 @@ L 129.15 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -880,11 +880,11 @@ L 148.68 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -901,11 +901,11 @@ L 168.21 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -922,11 +922,11 @@ L 187.74 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -943,11 +943,11 @@ L 207.27 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -966,11 +966,11 @@ L 226.8 217.728 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -981,11 +981,11 @@ L 226.8 346.249189 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -996,11 +996,11 @@ L 226.8 315.722526 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -1011,11 +1011,11 @@ L 226.8 285.195863 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -1026,11 +1026,11 @@ L 226.8 254.669199 +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 2.22,0.96; stroke-dashoffset: 0; stroke: #b0b0b0; stroke-width: 0.6"/> - + @@ -1088,7 +1088,7 @@ L 224.0658 330.228352 L 226.4094 330.770482 L 226.9953 330.875625 L 226.9953 330.875625 -" clip-path="url(#pdb55402bf3)" style="fill: none; stroke: #0000ff; stroke-linecap: square"/> +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke: #0000ff; stroke-linecap: square"/> +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 3.7,1.6; stroke-dashoffset: 0; stroke: #0000ff"/> +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke: #008000; stroke-linecap: square"/> +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 3.7,1.6; stroke-dashoffset: 0; stroke: #008000"/> +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke: #ff0000; stroke-linecap: square"/> +" clip-path="url(#pf9458ab0ca)" style="fill: none; stroke-dasharray: 3.7,1.6; stroke-dashoffset: 0; stroke: #ff0000"/> + - +