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Proceedings Paper

A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction
Author(s): Tetsuaki Matsunawa; Jhih-Rong Gao; Bei Yu; David Z. Pan
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Paper Abstract

Under the low-k1 lithography process, lithography hotspot detection and elimination in the physical verification phase have become much more important for reducing the process optimization cost and improving manufacturing yield. This paper proposes a highly accurate and low-false-alarm hotspot detection framework. To define an appropriate and simplified layout feature for classification model training, we propose a novel feature space evaluation index. Furthermore, by applying a robust classifier based on the probability distribution function of layout features, our framework can achieve very high accuracy and almost zero false alarm. The experimental results demonstrate the effectiveness of the proposed method in that our detector outperforms other works in the 2012 ICCAD contest in terms of both accuracy and false alarm.

Paper Details

Date Published: 18 March 2015
PDF: 11 pages
Proc. SPIE 9427, Design-Process-Technology Co-optimization for Manufacturability IX, 94270S (18 March 2015); doi: 10.1117/12.2085790
Show Author Affiliations
Tetsuaki Matsunawa, Toshiba Corp. (Japan)
Jhih-Rong Gao, The Univ. of Texas at Austin (United States)
Bei Yu, The Univ. of Texas at Austin (United States)
David Z. Pan, The Univ. of Texas at Austin (United States)


Published in SPIE Proceedings Vol. 9427:
Design-Process-Technology Co-optimization for Manufacturability IX
John L. Sturtevant; Luigi Capodieci, Editor(s)

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