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Design rule check is a critical step in the physical design of integratedcircuits to ensure manufacturability. However, it can be done only after atime-consuming detailed routing procedure, which adds drastically to the timeof design iterations. With advanced technology nodes, the outcomes of globalrouting and detailed routing become less correlated, which adds to thedifficulty of predicting design rule violations from earlier stages. In thispaper, a framework based on neural network ensembles is proposed to predictdesign rule violation hotspots using information from placement and globalrouting. A soft voting structure and a PCA-based subset selection scheme aredeveloped on top of a baseline neural network from a recent work. Experimentalresults show that the proposed architecture achieves significant improvement inmodel performance compared to the baseline case. For half of test cases, theperformance is even better than random forest, a commonly-used ensemblelearning model.

Design Rule Violation Hotspot Prediction Based on Neural Network Ensembles