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ASPDAC
2016
ACM

Laplacian eigenmaps and bayesian clustering based layout pattern sampling and its applications to hotspot detection and OPC

4 years 11 months ago
Laplacian eigenmaps and bayesian clustering based layout pattern sampling and its applications to hotspot detection and OPC
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 knowledges, such as highdimensional layout features and manually tuned hypothetical model parameters. In this paper 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 optica...
Tetsuaki Matsunawa, Bei Yu, David Z. Pan
Added 29 Mar 2016
Updated 29 Mar 2016
Type Journal
Year 2016
Where ASPDAC
Authors Tetsuaki Matsunawa, Bei Yu, David Z. Pan
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