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10 December 2024 Inverse lithography based on a physics-driven deep learning approach
Rui-xiang Chen, Yang Zhao, Rui Chen
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Proceedings Volume 13423, Eighth International Workshop on Advanced Patterning Solutions (IWAPS 2024); 134230F (2024) https://doi.org/10.1117/12.3052735
Event: 8th International Workshop on Advanced Patterning Solutions (IWAPS 2024), 2024, Jiaxing, Zhejiang, China
Abstract
Optical proximity correction (OPC) is essential for advanced semiconductor manufacturing beyond 45nm node by improving lithography resolution. However, traditional OPC techniques are limited by scalar imaging models and gradient-based algorithms. This study introduces a physics-driven deep learning OPC scheme that leverages vector imaging model. Guided by inverse lithography technology (ILT), the algorithm requires no extensive datasets, relying instead on varied, randomly generated patterns for training. The results from numerical testing confirm the approach's accuracy and efficiency.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Rui-xiang Chen, Yang Zhao, and Rui Chen "Inverse lithography based on a physics-driven deep learning approach", Proc. SPIE 13423, Eighth International Workshop on Advanced Patterning Solutions (IWAPS 2024), 134230F (10 December 2024); https://doi.org/10.1117/12.3052735
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KEYWORDS
Education and training
Lithography
Deep learning
Data modeling
Optical proximity correction
Imaging systems
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