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Journal of Micro/Nanolithography, MEMS, and MOEMS

Optical proximity correction with hierarchical Bayes model
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Paper Abstract

Optical proximity correction (OPC) is one of the most important techniques in today’s optical lithography-based manufacturing process. Although the most widely used model-based OPC is expected to achieve highly accurate correction, it is also known to be extremely time-consuming. This paper proposes a regression model for OPC using a hierarchical Bayes model (HBM). The goal of the regression model is to reduce the number of iterations in model-based OPC. Our approach utilizes a Bayes inference technique to learn the optimal parameters from given data. All parameters are estimated by the Markov Chain Monte Carlo method. Experimental results show that utilizing HBM can achieve a better solution than other conventional models, e.g., linear regression-based model, or nonlinear regression-based model. In addition, our regression results can be used as the starting point of conventional model-based OPC, through which we are able to overcome the runtime bottleneck.

Paper Details

Date Published: 11 March 2016
PDF: 8 pages
J. Micro/Nanolith. 15(2) 021009 doi: 10.1117/1.JMM.15.2.021009
Published in: Journal of Micro/Nanolithography, MEMS, and MOEMS Volume 15, Issue 2
Show Author Affiliations
Tetsuaki Matsunawa, Toshiba Corp. (Japan)
Bei Yu, The Chinese Univ. of Hong Kong (Hong Kong, China)
David Z. Pan, The Univ. of Texas at Austin (United States)


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