15 July 2021 Data augmentation in hotspot detection based on generative adversarial network
Shuhan Wang, Tianyang Gai, Tong Qu, Bojie Ma, Xiaojing Su, Lisong Dong, Libin Zhang, Peng Xu, Yajuan Su, Yayi Wei
Author Affiliations +
Funded by: National Natural Science Foundation of China (NSFC), National Nature Science Foundation of China
Abstract

Background: In datasets for hotspot detection in physical verification, data are predominantly composed of non-hotspot samples with only a small percentage of hotspot ones; this leads to the class imbalance problem, which usually hinders the performance of classifiers.

Aim: We aim to enrich datasets by applying a data augmentation technique.

Approach: We propose a data augmentation flow-based generative adversarial network (GAN) to generate high-resolution hotspot samples.

Results: We evaluated our flow with the current state-of-the-art convolutional neural network hotspot classifier by comparison with conventional data augmentation techniques. Experimental results demonstrate that the accuracy improvement of our work can reach 3% at the same false alarm rate and the false alarm rate reduction can reach 5% at the same accuracy.

Conclusions: Our study demonstrates that rational hotspot classification can improve the efficiency of data. It also highlights the potential of GAN to generate complicated layout patterns.

© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE) 1932-5150/2021/$28.00 © 2021 SPIE
Shuhan Wang, Tianyang Gai, Tong Qu, Bojie Ma, Xiaojing Su, Lisong Dong, Libin Zhang, Peng Xu, Yajuan Su, and Yayi Wei "Data augmentation in hotspot detection based on generative adversarial network," Journal of Micro/Nanopatterning, Materials, and Metrology 20(3), 034201 (15 July 2021). https://doi.org/10.1117/1.JMM.20.3.034201
Received: 29 January 2021; Accepted: 8 July 2021; Published: 15 July 2021
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KEYWORDS
Gallium nitride

Data modeling

Performance modeling

Binary data

Machine learning

Computer programming

Convolution

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