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arXiv · 2012.11510

Design Rule Checking with a CNN Based Feature Extractor

Luis Francisco·Tanmay Lagare·Arpit Jain·Somal Chaudhary·Madhura Kulkarni·Divya Sardana·W. Rhett Davis·Paul Franzon
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Abstract

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.

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Luis Francisco, Tanmay Lagare, Arpit Jain, Somal Chaudhary, Madhura Kulkarni, Divya Sardana, W. Rhett Davis, Paul Franzon. 2020-12-21. Design Rule Checking with a CNN Based Feature Extractor. https://arxiv.org/abs/2012.11510

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