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Proceedings Paper

A physics-driven complex valued neural network (CVNN) model for lithographic analysis
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Paper Abstract

Recent advances in machine learning and deep learning have provided an opportunity for improvement in the field of lithography. Compared with the numerical simulation, machine learning/deep learning may provide much faster and more efficient performance, but they provide less accurate solution due to the limitation of the statistical approach. Typical machine learning models cannot take into account complex multiple processes such as UV exposure, photoreaction and photo-resist development in lithography. In this work, we developed a newly designed deep learning algorithm not only to improve the model accuracy but also to overcome data limitation. By combining a physics-driven machine learning model and a complex-valued neural network (CVNN), we designed a novel machine learning model structure. Applying CVNN to phase shift mask analysis could improve the model performance dramatically. As such, this work opens up a new class of photo-lithography analysis by using a novel neural network model.

Paper Details

Date Published: 23 March 2020
PDF: 8 pages
Proc. SPIE 11326, Advances in Patterning Materials and Processes XXXVII, 113260I (23 March 2020); doi: 10.1117/12.2551697
Show Author Affiliations
Heehwan Lee, Samsung Display Co., Ltd. (Korea, Republic of)
Minjong Hong, Samsung Display Co., Ltd. (Korea, Republic of)
Min Kang, Samsung Display Co., Ltd. (Korea, Republic of)
Hyun Sung Park, Samsung Display Co., Ltd. (Korea, Republic of)
Kyusu Ahn, Samsung Display Co., Ltd. (Korea, Republic of)
Yongwoo Lee, Samsung Display Co., Ltd. (Korea, Republic of)
Yongjo Kim, Samsung Display Co., Ltd. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11326:
Advances in Patterning Materials and Processes XXXVII
Roel Gronheid; Daniel P. Sanders, Editor(s)

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