A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction

Tetsuaki Matsunawa; Jhih-Rong Gao; Bei Yu; David Z. Pan

doi:10.1117/12.2085790

18 March 2015 A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction

Tetsuaki Matsunawa, Jhih-Rong Gao, Bei Yu, David Z. Pan

Author Affiliations +

Proceedings Volume 9427, Design-Process-Technology Co-optimization for Manufacturability IX; 94270S (2015) https://doi.org/10.1117/12.2085790
Event: SPIE Advanced Lithography, 2015, San Jose, California, United States

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.

Citation Download Citation

Tetsuaki Matsunawa, Jhih-Rong Gao, Bei Yu, and David Z. Pan "A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction", Proc. SPIE 9427, Design-Process-Technology Co-optimization for Manufacturability IX, 94270S (18 March 2015); https://doi.org/10.1117/12.2085790

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