# Design Rule Checking with a CNN Based Feature Extractor

arXiv: https://arxiv.org/abs/2012.11510 · HTML: https://mlsanity.com/paper/2012.11510/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Review of Machine Learning for Micro-Electronic Design Verification](https://mlsanity.com/paper/2503.11687/) (2025)
- [Report for NSF Workshop on AI for Electronic Design Automation](https://mlsanity.com/paper/2601.14541/) (2026)
- [MAGNet: A Multi-Scale Attention-Guided Graph Fusion Network for DRC Violation Detection](https://mlsanity.com/paper/2506.07126/) (2025)

## Abstract (arXiv)

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.