Browsing by Author "Lin, Geng-He"

Now showing 1 - 2 of 2

Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction
(2013)
Yu, Yen-Ting
;
Lin, Geng-He
;
Jiang, Iris Hui-Ru
;
Chiang, Charles
;
電子工程學系及電子研究所
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.
Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction
(2015-03-01)
Yu, Yen-Ting
;
Lin, Geng-He
;
Jiang, Iris Hui-Ru
;
Chiang, Charles
;
電子工程學系及電子研究所
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.