11 March 2016 Optical proximity correction with hierarchical Bayes model
Author Affiliations +
J. of Micro/Nanolithography, MEMS, and MOEMS, 15(2), 021009 (2016). doi:10.1117/1.JMM.15.2.021009
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.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Tetsuaki Matsunawa, Bei Yu, David Z. Pan, "Optical proximity correction with hierarchical Bayes model," Journal of Micro/Nanolithography, MEMS, and MOEMS 15(2), 021009 (11 March 2016). https://doi.org/10.1117/1.JMM.15.2.021009


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