Distributed optical proximity correction with deep-learning lithographic model for i-line photolithography

Wei-Ping Liao; Yu-Fan Lin; Hsueh-Li Liu; Shu-De Gong; Peichen Yu; You-Chia Chang; Jia-Min Shieh; Chun-Chi Chen; Wen-Hsien Huang

doi:10.1117/12.2593408

1 August 2021 Distributed optical proximity correction with deep-learning lithographic model for i-line photolithography

Wei-Ping Liao, Yu-Fan Lin, Hsueh-Li Liu, Shu-De Gong, Peichen Yu, You-Chia Chang, Jia-Min Shieh, Chun-Chi Chen, Wen-Hsien Huang

Author Affiliations +

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180421 (2021) https://doi.org/10.1117/12.2593408
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Conference Poster

Abstract

In this study, we propose a deep-learning approach to establish the lithographic model for i-line photolithography and develop an optical proximity correction (OPC) algorithm to increase the resolution limit. The applications of RETs are not only on CMOS semiconductor, but also on some metasurface which used to patterning by electron beam lithography. With the OPC algorithm, we are able to manufacture a near-infrared metalens patterning by i-line photolithography in a more efficient and less expensive way.

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Wei-Ping Liao, Yu-Fan Lin, Hsueh-Li Liu, Shu-De Gong, Peichen Yu, You-Chia Chang, Jia-Min Shieh, Chun-Chi Chen, and Wen-Hsien Huang "Distributed optical proximity correction with deep-learning lithographic model for i-line photolithography", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180421 (1 August 2021); https://doi.org/10.1117/12.2593408

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