An Efficient Approach for DRC Hotspot Prediction with Convolutional Neural Network

Predicting the design rule check (DRC) violation hotspots in an early stage plays an essential role in the efficiency of the physical design. Multiple factors that affect the performance of a DRC hotspot predictor, among them, the efficacy of the extracted features plays a substantial role. In this paper, we propose a connectivity-based DRC hotspot prediction method using a convolutional neural network. We show that the proposed method is efficient in both training and prediction. The relation between pin features and predictor performance is further investigated and two weighted connectivity-based route map features are introduced. Experimental results demonstrate that the proposed algorithm can predict on average 73% of the DRC hotspots with only 2.7% false alarms.