Low-Cost Lithography Hotspot Detection with Active Entropy Sampling and Model Calibration

With feature size scaling and complexity increase of circuit designs, hotspot detection has become a significant challenge in the very-large-scale-integration (VLSI) industry. Traditional detection methods, such as pattern matching and machine learning, have been made a remarkable progress. However, the performance of classifiers relies heavily on reference layout libraries, leading to the high cost of lithography simulation. Querying and sampling qualified candidates from raw datasets make active learning-based strategies serve as an effective solution in this field, but existing relevant studies fail to take sufficient sampling criteria into account. In this paper, embedded in pattern sampling and hotspot detection framework, an entropy-based batch mode sampling strategy is proposed in terms of calibrated model uncertainty and data diversity to handle the hotspot detection problem. Redundant patterns can be effectively avoided, and the classifier can converge with high celerity. Experiment results show that our method outperforms previous works in both ICCAD2012 and ICCAD2016 Contest benchmarks, achieving satisfactory detection accuracy and significantly reduced lithography simulation overhead.