Multi-level layout hotspot detection based on multi-classification with deep learning

Tianyang Gai; Tong Qu; Xiaojing Su; Shuhan Wang; Lisong Dong; Libin Zhang; Rui Chen; Yajuan Su; Yayi Wei; Tianchun Ye

doi:10.1117/12.2583726

22 February 2021 Multi-level layout hotspot detection based on multi-classification with deep learning

Tianyang Gai, Tong Qu, Xiaojing Su, Shuhan Wang, Lisong Dong, Libin Zhang, Rui Chen, Yajuan Su, Yayi Wei, Tianchun Ye

Proceedings Volume 11614, Design-Process-Technology Co-optimization XV; 116140W (2021) https://doi.org/10.1117/12.2583726
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

With the development of process technology nodes, hotspot detection has become a critical process in integrated circuit physical design flow. The machine learning-based method has become a competitive candidate for layout hotspot detector with easy training and high speed. Classic methods usually define hotspot detection as a binary classification problem. However, the designer hopes to further divide the hotspot patterns into a series of levels according to their severity to identify and fix these hotspots. In this paper, we designed a multi-classifier based on the convolutional neural network to realize the detection of various levels of hotspot patterns. Unlike classic cross-entropy loss, we proposed a custom loss function to reduce the difference between false predicted levels and corresponding true levels, reducing the adverse effects caused by misclassified samples. Experimental verification results show that our hotspot detector can correctly classify various hotspots levels and has potential advantages for physical designers to fix hotspots.

Conference Presentation

Citation Download Citation

Tianyang Gai, Tong Qu, Xiaojing Su, Shuhan Wang, Lisong Dong, Libin Zhang, Rui Chen, Yajuan Su, Yayi Wei, and Tianchun Ye "Multi-level layout hotspot detection based on multi-classification with deep learning", Proc. SPIE 11614, Design-Process-Technology Co-optimization XV, 116140W (22 February 2021); https://doi.org/10.1117/12.2583726

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