@inproceedings{410d0ff6da2346eb974504002d69e872,
title = "Litho-neuralode: Improving hotspot detection accuracy with advanced data augmentation and neural ordinary differential equations",
abstract = "The use of deep neural networks in pattern matching has tremendously improved the accuracy of the lithographic hotspot detection, preventing any catastrophic chip failure. In this paper, we propose three data augmentation techniques (“Translation”, “Gaussian noise”, and “Fill shapes”) to deal with the imbalance outlier lithographic hotspot problem and adopt the neural ordinary differential equations networks (Litho-NeuralODE) to improve the detection accuracy. Our architecture uses 28×28 pixel clips to perform the hotspot classification. Experimental result on ICCAD 2012 Contest benchmarks shows that our proposed framework achieves the overall highest accuracy of 98.7\% and the lowest misses of 10 on average, outperforming the state-of-the-art works.",
keywords = "Deep Neural Network, Design for Manufacturability, Feature Extraction, Lithography Hotspot Detection",
author = "Wei Lu and Yuhang Zhang and Qing Zhang and Xinjie Zhang and Yongfu Li",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computing Machinery.; 30th Great Lakes Symposium on VLSI, GLSVLSI 2020 ; Conference date: 07-09-2020 Through 09-09-2020",
year = "2020",
month = sep,
day = "7",
doi = "10.1145/3386263.3406937",
language = "英语",
series = "Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI",
publisher = "Association for Computing Machinery",
pages = "387--392",
booktitle = "GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI",
address = "美国",
}