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Journal of Micro/Nanolithography, MEMS, and MOEMS

Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering<xref ref-type="fn" rid="fn1" /<
Author(s): Bei Yu; Jhih-Rong Gao; Duo Ding; Xuan Zeng; David Z. Pan
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Paper 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.

Paper Details

Date Published: 4 November 2014
PDF: 12 pages
J. Micro/Nanolith. 14(1) 011003 doi: 10.1117/1.JMM.14.1.011003
Published in: Journal of Micro/Nanolithography, MEMS, and MOEMS Volume 14, Issue 1
Show Author Affiliations
Bei Yu, The Univ. of Texas at Austin (United States)
Jhih-Rong Gao, The Univ. of Texas at Austin (United States)
Duo Ding, Oracle Microelectronics (United States)
Xuan Zeng, Fudan Univ. (China)
David Z. Pan, The Univ. of Texas at Austin (United States)


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