| Autor: |
Libin Zhang, Yayi Wei, Xiaojing Su, Tong Qu, Yajuan Su, Lisong Dong, Bojie Ma, Peng Xu, Tianyang Gai, Shuhan Wang |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Journal of Micro/Nanopatterning, Materials, and Metrology. 20 |
| ISSN: |
2708-8340 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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