Machine-learning-based hotspot detection using topological classification and critical feature extraction

• Published in: 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC) pp. 1 - 6
• Main Authors: Yu, Yen-Ting, Lin, Geng-He, Jiang, Iris Hui-Ru, Chiang, Charles
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 29.05.2013; IEEE
• Series: ACM Conferences
• Subjects: Accuracy; Design for manufacturability; Feature extraction; fuzzy pattern matching; Hardware > Electronic design automation > Physical design (EDA); hotspot detection; Kernel; Layout; lithography hotspot; machine learning; support vector machine; Support vector machines; Training; Training data; design for manufacturability; fuzzy pattern matching; support vector machine; hotspot detection; machine learning; lithography hotspot
• ISBN: 1450320716; 9781450320719
• ISSN: 0738-100X
• DOI: 10.1145/2463209.2488816

Because of the widening sub-wavelength lithography gap in advanced fabrication technology, lithography hotspot detection has become an essential task in design for manufacturability. Current state-of-the-art works unite pattern matching and machine learning engines. Unlike them, 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. After detection, we filter hotspots 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 ICCAD winner on accuracy and false alarm.