Poster + Paper
28 April 2023 Lithography hotspot detection based on residual network
Author Affiliations +
Conference Poster
Abstract
Lithography hotspot detection is a key step in VLSI physical verification flow. In this paper, we propose a hotspot detection method based on new data augmentation, residual network and pretrained network models. The residual network preserves the depth of the deep convolutional neural network while taking the advantages of the shallow network, thus avoiding network degradation and improving the learning ability of hotspot features. We also apply data augmentation methods to increase the number of hotspot samples, so that the model can be trained with balanced data and prevent neural network overfitting. Our research shows that the proposed network’s improved performance and efficiency over prevailing approaches show a strong candidature for lithographic hotspot detection.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mu Lin, Fanwenqing Zeng, Yijiang Shen, and Yayi Wei "Lithography hotspot detection based on residual network", Proc. SPIE 12495, DTCO and Computational Patterning II, 124951M (28 April 2023); https://doi.org/10.1117/12.2657644
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KEYWORDS
Education and training

Data modeling

Lithography

Machine learning

Performance modeling

Feature extraction

Statistical modeling

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