  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.
