Laplacian eigenmaps- and Bayesian clustering-based layout pattern sampling and its applications to hotspot detection and optical proximity correction-Reference-Cited by-同舟云学术

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

Published:2016-10-21 Issue:4 Volume:15 Page:043504
ISSN:1932-5150
Container-title:Journal of Micro/Nanolithography, MEMS, and MOEMS
language:en
Short-container-title:J. Micro/Nanolith. MEMS MOEMS

Author:

Matsunawa Tetsuaki¹,Yu Bei²,Pan David Z.³

Affiliation:

1. Toshiba Corporation, Yokohama 247-8585, Japan

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

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

Publisher

SPIE-Intl Soc Optical Eng

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Condensed Matter Physics,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference24 articles.

1. Machine learning and pattern matching in physical design

2. Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis

3. EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation

4. Directed self-assembly (DSA) template pattern verification;Xiao,2014

5. Synthesis of lithography test patterns through topology-oriented pattern extraction and classification

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