Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering

Bei Yu; Jhih-Rong Gao; Duo Ding; Xuan Zeng; David Z. Pan

doi:10.1117/1.JMM.14.1.011003

4 November 2014 Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering

Bei Yu, Jhih-Rong Gao, Duo Ding, Xuan Zeng, David Z. Pan

Author Affiliations +

Journal of Micro/Nanolithography, MEMS, and MOEMS, Vol. 14, Issue 1, 011003 (November 2014). https://doi.org/10.1117/1.JMM.14.1.011003

Abstract

As technology nodes continue to shrink, layout patterns become more sensitive to lithography processes, resulting in lithography hotspots that need to be identified and eliminated during physical verification. We propose an accurate hotspot detection approach based on principal component analysis-support vector machine classifier. Several techniques, including hierarchical data clustering, data balancing, and multilevel training, are provided to enhance the performance of the proposed approach. Our approach is accurate and more efficient than conventional time-consuming lithography simulation and provides a high flexibility for adapting to new lithography processes and rules.

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Bei Yu, Jhih-Rong Gao, Duo Ding, Xuan Zeng, and David Z. Pan "Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering," Journal of Micro/Nanolithography, MEMS, and MOEMS 14(1), 011003 (4 November 2014). https://doi.org/10.1117/1.JMM.14.1.011003

Published: 4 November 2014

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