Optical proximity correction with hierarchical Bayes model

Tetsuaki Matsunawa; Bei Yu; David Z. Pan

doi:10.1117/1.JMM.15.2.021009

11 March 2016 Optical proximity correction with hierarchical Bayes model

Tetsuaki Matsunawa, Bei Yu, David Z. Pan

Journal of Micro/Nanolithography, MEMS, and MOEMS, Vol. 15, Issue 2, 021009 (March 2016). https://doi.org/10.1117/1.JMM.15.2.021009

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.

Citation Download Citation

Tetsuaki Matsunawa, Bei Yu, and 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

Published: 11 March 2016

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