DRC Hotspot Prediction at Sub-10nm Process Nodes Using Customized Convolutional Network

Abstract

As the semiconductor process technology advances into sub-10nm regime, cell pin accessibility, which is a complex joint effect from the pin shape and nearby blockages, becomes a main cause for DRC violations. Therefore, a machine learning model for DRC hotspot prediction needs to consider both very high-resolution pin shape patterns and low-resolution layout information as input features. A new convolutional neural network technique, J-Net, is introduced for the prediction with mixed resolution features. This is a customized architecture that is flexible for handling various input and output resolution requirements. It can be applied at placement stage without using global routing information. This technique is evaluated on 12 industrial designs at 7nm technology node. The results show that it can improve true positive rate by 37%, 40% and 14% respectively, compared to three recent works, with similar false positive rates.