Design Rule Checking with a CNN Based Feature Extractor
This work addresses the problem of increasingly complex and slow design rule checking for layout engineers, offering a faster interactive solution.
This paper demonstrates the feasibility of a fast interactive design rule checking (DRC) engine using a convolutional neural network (CNN). The CNN model, trained on artificial data from 50 SRAM designs, can detect multiple metal 1 DRC violations 32x faster than Boolean checkers with up to 92% accuracy.
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.