Lithography hotspot detection based on residual network

Mu Lin; Fanwenqing Zeng; Yijiang Shen; Yayi Wei

doi:10.1117/12.2657644

28 April 2023 Lithography hotspot detection based on residual network

Mu Lin, Fanwenqing Zeng, Yijiang Shen, Yayi Wei

Proceedings Volume 12495, DTCO and Computational Patterning II; 124951M (2023) https://doi.org/10.1117/12.2657644
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

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.

Citation Download Citation

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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