Bayesian analysis for OPC modeling with film stack properties and posterior predictive checking

Andrew Burbine; Germain Fenger; John Sturtevant; David Fryer

doi:10.1117/12.2249680

20 October 2016 Bayesian analysis for OPC modeling with film stack properties and posterior predictive checking

Andrew Burbine, Germain Fenger, John Sturtevant, David Fryer

Proceedings Volume 10032, 32nd European Mask and Lithography Conference; 100320N (2016) https://doi.org/10.1117/12.2249680
Event: 32nd European Mask and Lithography Conference, 2016, Dresden, Germany

Abstract

The use of optical proximity correction (OPC) demands increasingly accurate models of the photolithographic process. Model building and analysis techniques in the data science community have seen great strides in the past two decades which make better use of available information. This paper expands upon Bayesian analysis methods for parameter selection in lithographic models by increasing the parameter set and employing posterior predictive checks. Work continues with a Markov chain Monte Carlo (MCMC) search algorithm to generate posterior distributions of parameters. Models now include wafer film stack refractive indices, n and k, as parameters, recognizing the uncertainties associated with these values. Posterior predictive checks are employed as a method to validate parameter vectors discovered by the analysis, akin to cross validation.

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Andrew Burbine, Germain Fenger, John Sturtevant, and David Fryer "Bayesian analysis for OPC modeling with film stack properties and posterior predictive checking", Proc. SPIE 10032, 32nd European Mask and Lithography Conference, 100320N (20 October 2016); https://doi.org/10.1117/12.2249680

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KEYWORDS

Data modeling

Optical proximity correction

Refractive index

Calibration

Statistical modeling

Photomasks

Semiconducting wafers

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