Special Section on Continuation of Scaling with Optical and Complementary Lithography

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

[+] Author Affiliations
Bei Yu

University of Texas at Austin, Department of Electrical and Computer Engineering, Austin, Texas 78712, United States

Jhih-Rong Gao

University of Texas at Austin, Department of Electrical and Computer Engineering, Austin, Texas 78712, United States

Duo Ding

Oracle Microelectronics, Austin, Texas 78727, United States

Xuan Zeng

Fudan University, State Key Laboratory of ASIC & Systems, Microelectronics Department, Shanghai 200433, China

David Z. Pan

University of Texas at Austin, Department of Electrical and Computer Engineering, Austin, Texas 78712, United States

J. Micro/Nanolith. MEMS MOEMS. 14(1), 011003 (Nov 04, 2014). doi:10.1117/1.JMM.14.1.011003
History: Received June 28, 2014; Revised August 29, 2014; Accepted September 24, 2014
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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.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

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", J. Micro/Nanolith. MEMS MOEMS. 14(1), 011003 (Nov 04, 2014). ; http://dx.doi.org/10.1117/1.JMM.14.1.011003


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