Lithography

Accurate lithography hotspot detection using deep convolutional neural networks

[+] Author Affiliations
Moojoon Shin

Sungkyunkwan University, Department of Electrical and Computer Engineering, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea

Samsung Electronics, San #16, Banwol-dong, Hwasung 445-701, Republic of Korea

Jee-Hyong Lee

Sungkyunkwan University, Department of Electrical and Computer Engineering, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea

J. Micro/Nanolith. MEMS MOEMS. 15(4), 043507 (Nov 18, 2016). doi:10.1117/1.JMM.15.4.043507
History: Received May 17, 2016; Accepted October 25, 2016
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Abstract.  As the physical design of semiconductors continues to shrink, the lithography process is becoming more sensitive to layout design. Identifying lithography hotspots (HSs) in the layout design stage appears to be more and more crucial for fast semiconductor development. In this direction, we propose an accurate HS detection method using convolutional neural networks. Our approach produces more accurate detection performance (95.5% recall and 22.2% precision) compared to previous approaches. In spite of the use of deep convolutional neural networks, our method achieves a fast detection time of 0.72  h/mm2. In order to quickly and accurately detect HSs, we not only utilize the nature of convolutional-neural networks but also make additional technical efforts to improve the performance of our framework, including inspection region reduction, data augmentation, DBSCAN clustering, modified batch normalization, and fast image scanning. To the best of our knowledge, our approach is the first CNN-based lithography HS detection.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Moojoon Shin and Jee-Hyong Lee
"Accurate lithography hotspot detection using deep convolutional neural networks", J. Micro/Nanolith. MEMS MOEMS. 15(4), 043507 (Nov 18, 2016). ; http://dx.doi.org/10.1117/1.JMM.15.4.043507


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