Special Section on Photomask Manufacturing Technology

Optical proximity correction with hierarchical Bayes model

[+] Author Affiliations
Tetsuaki Matsunawa

Toshiba Corporation, Yokohama 247-8585, Japan

Bei Yu

The Chinese University of Hong Kong, CSE Department, NT, Hong Kong

David Z. Pan

The University of Texas at Austin, ECE Department, Austin, Texas 78712, United States

J. Micro/Nanolith. MEMS MOEMS. 15(2), 021009 (Mar 11, 2016). doi:10.1117/1.JMM.15.2.021009
History: Received October 26, 2015; Accepted February 16, 2016
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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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Tetsuaki Matsunawa ; Bei Yu and David Z. Pan
"Optical proximity correction with hierarchical Bayes model", J. Micro/Nanolith. MEMS MOEMS. 15(2), 021009 (Mar 11, 2016). ; http://dx.doi.org/10.1117/1.JMM.15.2.021009


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