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Proceedings Paper

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

Paper Details

Date Published: 20 October 2016
PDF: 5 pages
Proc. SPIE 10032, 32nd European Mask and Lithography Conference, 100320N (20 October 2016); doi: 10.1117/12.2249680
Show Author Affiliations
Andrew Burbine, Mentor Graphics Corp. (United States)
Germain Fenger, Mentor Graphics Corp. (United States)
John Sturtevant, Mentor Graphics Corp. (United States)
David Fryer, Mentor Graphics Corp. (United States)

Published in SPIE Proceedings Vol. 10032:
32nd European Mask and Lithography Conference
Uwe F.W. Behringer; Jo Finders, Editor(s)

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