Transforming Global Routing Report into DRC Violation Map with Convolutional Neural Network
Wei-Tse Hung, Junyang Huang, Yih-Chih Chou, Cheng-Hong Tsai, Mango C.-T. Chao
Abstract
In this paper, we have proposed a machine-learning framework to predict the DRC-violation map of a given design resulting from its detailed routing based on the congestion report resulting from its global routing. The proposed framework utilizes convolutional neural network as its core technique to train this prediction model. The training dataset is collected from 15 industrial designs using a leading commercial APR tool, and the total number of collected training samples exceed 26M. A specialized under-sampling technique is proposed to select important training samples for learning, compensate for the inaccuracy misled by a highly imbalanced training dataset, and speed up the entire training process. The experimental result demonstrates that our trained model can result in not only a significantly higher accuracy than previous related works but also a DRC violation map visually matching the actual ones closely. The average runtime of using our learned model to generate a DRC-violation map is only 3% of that of global routing, and hence our proposed framework can be viewed as a simple add-on tool to a current commercial global router that can efficiently and effectively generate a more realistic DRC-violation map without really applying detailed routing.