Imbalance aware lithography hotspot detection: a deep learning approach-Reference-Cited by-同舟云学术

Imbalance aware lithography hotspot detection: a deep learning approach

Published:2017-08-24 Issue:03 Volume:16 Page:1
ISSN:1932-5150
Container-title:Journal of Micro/Nanolithography, MEMS, and MOEMS
language:
Short-container-title:J. Micro/Nanolith. MEMS MOEMS

Author:

Yang Haoyu¹,Luo Luyang¹,Su Jing²,Lin Chenxi²,Yu Bei¹

Affiliation:

1. Chinese University of Hong Kong, Department of Computer Science and Engineering, New Territories

2. ASML Brion Inc., San Jose, California

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

Reference41 articles.

1. Hotspot detection on post-OPC layout using full-chip simulation-based verification tool: a case study with aerial image simulation

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3. Accurate process-hotspot detection using critical design rule extraction;Yu,2012

4. A Fuzzy-Matching Model With Grid Reduction for Lithography Hotspot Detection

5. High Performance Lithography Hotspot Detection With Successively Refined Pattern Identifications and Machine Learning

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