Automatic layout feature extraction for lithography hotspot detection based on deep neural network-Reference-Cited by-同舟云学术

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Automatic layout feature extraction for lithography hotspot detection based on deep neural network

Published:2016-03-16 Issue: Volume: Page:
ISSN:0277-786X
Container-title:SPIE Proceedings
language:
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Author:

Matsunawa Tetsuaki,Nojima Shigeki,Kotani Toshiya

Publisher

SPIE

Reference16 articles.

1. Pushing the limits of lithography

2. Self-assembly patterning for sub-15nm half-pitch: a transition from lab to fab;Bencher,2011

3. Fast dual graph based hotspot detection;Kahng,2006

4. Combination of rule and pattern based lithography unfriendly pattern detection in opc flow;Kang,2008

5. Efficient process-hotspot detection using range pattern matching;Yao,2006

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1. Lithography Hotspot Detection Based on Improved YOLOv3;ACTA OPT SIN;2023

2. Geometrical positioning surveying-based features for BEOL line-end-pull-back modeling using regression-based machine-learning;Journal of Micro/Nanopatterning, Materials, and Metrology;2023-06-29

3. Layout pattern analysis and coverage evaluation in computational lithography;Optics Express;2023-02-23

4. 基于预训练VGG11模型的光刻坏点检测方法;Acta Optica Sinica;2023

5. Can optical proximity correction solution be learned? The learning limit and a general learning framework;Journal of Micro/Nanopatterning, Materials, and Metrology;2022-11-29

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