Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction

Because of the widening sub-wavelength lithography gap in advanced fabrication technology, lithography hotspot detection has become an essential task in design for manufacturability. Unlike current state-of-the-art works, which unite pattern matching and machine-learning engines, we fully exploit the strengths of machine learning using novel techniques. By combing topological classification and critical feature extraction, our hotspot detection framework achieves very high accuracy. Furthermore, to speed-up the evaluation, we verify only possible layout clips instead of full-layout scanning. We utilize feedback learning and present redundant clip removal to reduce the false alarm. Experimental results show that the proposed framework is very accurate and demonstrates a rapid training convergence. Moreover, our framework outperforms the 2012 CAD contest at International Conference on Computer-Aided Design (ICCAD) winner on accuracy and false alarm.