Laplacian eigenmaps- and Bayesian clustering-based layout pattern sampling and its applications to hotspot detection and optical proximity correction

Tetsuaki Matsunawa; Bei Yu; David Z. Pan

doi:10.1117/1.JMM.15.4.043504

21 October 2016 Laplacian eigenmaps- and Bayesian clustering-based layout pattern sampling and its applications to hotspot detection and optical proximity correction

Tetsuaki Matsunawa, Bei Yu, David Z. Pan

Author Affiliations +

Journal of Micro/Nanolithography, MEMS, and MOEMS, Vol. 15, Issue 4, 043504 (October 2016). https://doi.org/10.1117/1.JMM.15.4.043504

Abstract

Effective layout pattern sampling is a fundamental component for lithography process optimization, hotspot detection, and model calibration. Existing pattern sampling algorithms rely on either vector quantization or heuristic approaches. However, it is difficult to manage these methods due to the heavy demands of prior knowledge, such as high-dimensional layout features and manually tuned hypothetical model parameters. We present a self-contained layout pattern sampling framework, where no manual parameter tuning is needed. To handle high dimensionality and diverse layout feature types, we propose a nonlinear dimensionality reduction technique with kernel parameter optimization. Furthermore, we develop a Bayesian model-based clustering, through which automatic sampling is realized without arbitrary setting of model parameters. The effectiveness of our framework is verified through a sampling benchmark suite and two applications: lithography hotspot detection and optical proximity correction.

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Tetsuaki Matsunawa, Bei Yu, and David Z. Pan "Laplacian eigenmaps- and Bayesian clustering-based layout pattern sampling and its applications to hotspot detection and optical proximity correction," Journal of Micro/Nanolithography, MEMS, and MOEMS 15(4), 043504 (21 October 2016). https://doi.org/10.1117/1.JMM.15.4.043504

Published: 21 October 2016

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