18 March 2015 Optical proximity correction with hierarchical Bayes model
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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 non-linear regression based model. In addition, our regression results can be fed as the starting point of conventional model based OPC, through which we are able to overcome the runtime bottleneck.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tetsuaki Matsunawa, Tetsuaki Matsunawa, Bei Yu, Bei Yu, David Z. Pan, David Z. Pan, "Optical proximity correction with hierarchical Bayes model", Proc. SPIE 9426, Optical Microlithography XXVIII, 94260X (18 March 2015); doi: 10.1117/12.2085787; https://doi.org/10.1117/12.2085787


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