Machine learning based inverse lithography technology for an advanced DRAM contact layer

Moongyu Jeong; Hyunchul Kim; Kyle Braam; Munhoe Do; Kangjin Kim; Jongcheon Park; Chunsoo Kang; Chanha Park; Thomas Cecil

doi:10.1117/12.2657808

30 April 2023 Machine learning based inverse lithography technology for an advanced DRAM contact layer

Moongyu Jeong, Hyunchul Kim, Kyle Braam, Munhoe Do, Kangjin Kim, Jongcheon Park, Chunsoo Kang, Chanha Park, Thomas Cecil

Author Affiliations +

Proceedings Volume 12495, DTCO and Computational Patterning II; 124951E (2023) https://doi.org/10.1117/12.2657808
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

Machine Learning (ML) based technologies are actively being adopted in the computational lithography domain. ML-based methods have the potential to enhance the accuracy of predictive models, speed up the run-times of the mask optimization processes and produce consistent results compared with the other numerical methods. In this paper, we present the result of an ML-based ILT application to an advanced DRAM contact layer for both core and periphery region. In our ML ILT method, golden mask layouts are generated by ProteusTM ILT tool for the sampled target layouts to obtain reliable training inputs, which are then used to train a custom-designed Convolutional Neural Network (CNN). The trained CNN is plugged-in to the conventional ILT flow as an initial mask provider and the entire

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Citation Download Citation

Moongyu Jeong, Hyunchul Kim, Kyle Braam, Munhoe Do, Kangjin Kim, Jongcheon Park, Chunsoo Kang, Chanha Park, and Thomas Cecil "Machine learning based inverse lithography technology for an advanced DRAM contact layer", Proc. SPIE 12495, DTCO and Computational Patterning II, 124951E (30 April 2023); https://doi.org/10.1117/12.2657808

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