Design rule checking (DRC) is getting increasingly complex in advanced nodes technologies. It would be highly desirable to have a fast interactive DRC engine that could be used during layout. In this work, we establish the proof of feasibility for such an engine. The proposed model consists of a convolutional neural network (CNN) trained to detect DRC violations. The model was trained with artificial data that was derived from a set of 50 SRAM designs. The focus in this demonstration was metal 1 rules. Using this solution, we can detect multiple DRC violations 32x faster than Boolean checkers with an accuracy of up to 92. The proposed solution can be easily expanded to a complete rule set.
@article{arxiv.2012.11510,
title = {Design Rule Checking with a CNN Based Feature Extractor},
author = {Luis Francisco and Tanmay Lagare and Arpit Jain and Somal Chaudhary and Madhura Kulkarni and Divya Sardana and W. Rhett Davis and Paul Franzon},
journal= {arXiv preprint arXiv:2012.11510},
year = {2020}
}