This repository is the canonical ADC Performance Survey dataset maintained by Boris Murmann. It aggregates published ADC (analog-to-digital converter) performance data from ISSCC and VLSI Symposium papers between 1997 and 2026. The primary data artifact is an Excel workbook (xls/ADCsurvey_latest.xlsx) that is consumed by a set of Jupyter notebooks under plots/ to generate publication-quality figures such as FOMs plots, energy plots, aperture trend plots, and FOM trend plots. The repo also contains PDF resources and a short course presentation.
The CircuitNet repository provides an open-source dataset and associated code for machine learning in Electronic Design Automation (EDA). It focuses on four key EDA tasks: congestion prediction, design rule check (DRC) prediction, IR drop prediction, and net delay prediction. The repository includes scripts for extracting features from LEF/DEF files (via Innovus output), training and evaluating deep learning models (CNNs and GNNs) for these tasks, and utilities for data loading and model training. The dataset is distributed in multiple versions (CircuitNet-N28, N14, N45) and consists of preprocessed features and labels stored as NumPy arrays. The codebase is organized into modular components: feature extraction, task-specific model directories, and shared utilities. While the repository enables reproduction of published results, it relies on specific tool versions (Innovus) and has hardcoded paths, which may require adaptation for reuse in different environments.