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

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems Vol. 34; no. 3; pp. 460 - 470
• Main Authors: Yen-Ting Yu, Geng-He Lin, Jiang, Iris Hui-Ru, Chiang, Charles
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Classification; Clips; Computer aided design; Design engineering; Design for manufacturability; Feature extraction; fuzzy pattern matching; Hot spots; hotspot detection; Kernel; Layout; Lithography; lithography hotspot; machine learning; support vector machine; Support vector machines; Topology; Training; fuzzy pattern matching; hotspot detection; machine learning; Design for manufacturability; support vector machine (SVM); lithography hotspot
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2014.2387858

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 ComputerAided Design (ICCAD) winner on accuracy and false alarm.