4 November 2014 Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering<xref ref-type="fn" rid="fn1" /<
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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.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Bei Yu, Bei Yu, Jhih-Rong Gao, Jhih-Rong Gao, Duo Ding, Duo Ding, Xuan Zeng, Xuan Zeng, David Z. Pan, David Z. Pan, } "Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering<xref ref-type="fn" rid="fn1" /<," Journal of Micro/Nanolithography, MEMS, and MOEMS 14(1), 011003 (4 November 2014). https://doi.org/10.1117/1.JMM.14.1.011003 . Submission:
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