Browsing by Author "Chiang, Charles"

Now showing 1 - 4 of 4

Accurate Process-Hotspot Detection Using Critical Design Rule Extraction
(2012)
Yu, Yen-Ting
;
Chan, Ya-Chung
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Sinha, Subarna
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Jiang, Iris Hui-Ru
;
Chiang, Charles
In advanced fabrication technology, the sub-wavelength lithography gap causes unwanted layout distortions. Even if a layout passes design rule checking (DRC), it still might contain process hotspots, which are sensitive to the lithographic process. Hence, process-hotspot detection has become a crucial issue. In this paper, we propose an accurate process-hotspot detection framework. Unlike existing DRC-based works, we extract only critical design rules to express the topological features of hotspot patterns. We adopt a two-stage filtering process to locate all hotspots accurately and efficiently. Compared with state-of-the-art DRC-based works, our results show that our approach can reach 100% success rate with significant speedups.
DRC-Based Hotspot Detection Considering Edge Tolerance and Incomplete Specification
(2014)
Yu, Yen-Ting
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Jiang, Iris Hui-Ru
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Zhang, Yumin
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Chiang, Charles
;
交大名義發表
To improve the yield in current manufacturing processes, the problematic layout configurations, so-called process-hotspots, should be detected and replaced with yield friendly patterns. A hotspot pattern with edge tolerances and incomplete specifications, where edges may vary in a certain range and any layout configurations may exist in its ambit regions, can sufficiently and generally represent a process-hotspot. This type of hotspots, however, cannot be efficiently or correctly detected by using the state-of-the-art string-matching-based method. In this paper, we present an accurate and efficient DRC-based hotspot detection framework to handle hotspot patterns with edge tolerances and incomplete specifications. Unlike existing DRC-based work, which handles only completely specified patterns, we extract critical design rules to represent all possible topologies of hotspot patterns with edge tolerances and incomplete specifications. We further order these rules to iteratively reduce the search regions of a layout during design rule checking. Then, we apply longest common subsequence and linear scan to locate all hotspots accurately and efficiently. Compared with the state-of-the-art work, experimental results show that our approach can reach promising success rates with significant speedups.
Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction
(2013)
Yu, Yen-Ting
;
Lin, Geng-He
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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.