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Machine learning-based lithography hotspot detection has been deeply studiedrecently, from varies feature extraction techniques to efficient learningmodels. It has been observed that such machine learning-based frameworks areproviding satisfactory metal layer hotspot prediction results on known publicmetal layer benchmarks. In this work, we seek to evaluate how these machinelearning-based hotspot detectors generalize to complicated patterns. We firstintroduce a automatic layout generation tool that can synthesize varies layoutpatterns given a set of design rules. The tool currently supports both metallayer and via layer generation. As a case study, we conduct hotspot detectionon the generated via layer layouts with representative machine learning-basedhotspot detectors, which shows that continuous study on model robustness andgenerality is necessary to prototype and integrate the learning engines in DFMflows. The source code of the layout generation tool will be available athttps://github. com/phdyang007/layout-generation.

Automatic Layout Generation with Applications in Machine Learning Engine Evaluation