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Data augmentation in hotspot detection based on generative adversarial network.
- Source :
-
Journal of Micro/Nanolithography, MEMS & MOEMS . Jul-Sep2021, Vol. 20 Issue 3, p34201-34201. 1p. - Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19325150
- Volume :
- 20
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Journal of Micro/Nanolithography, MEMS & MOEMS
- Publication Type :
- Academic Journal
- Accession number :
- 152789200
- Full Text :
- https://doi.org/10.1117/1.JMM.20.3.034201