Lithography

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

[+] Author Affiliations
Tetsuaki Matsunawa

Toshiba Corporation, Yokohama 247-8585, Japan

Bei Yu

The Chinese University of Hong Kong, Computer Science and Engineering Department, NT, Hong Kong

David Z. Pan

The University of Texas at Austin, Electrical and Computer Engineering Department, Austin, Texas 78712, United States

J. Micro/Nanolith. MEMS MOEMS. 15(4), 043504 (Oct 21, 2016). doi:10.1117/1.JMM.15.4.043504
History: Received July 4, 2016; Accepted September 28, 2016
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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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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

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", J. Micro/Nanolith. MEMS MOEMS. 15(4), 043504 (Oct 21, 2016). ; http://dx.doi.org/10.1117/1.JMM.15.4.043504


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