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22 February 2021 Validation of machine learning OPC compact models for advanced manufacturing
Moongyu Jeong, Marco Guajardo, Cheng-En Wu, Song-haeng Lee, Tim Fuehner, Lena Zavyalova, Li-Jin Chen, Hua Song, Kevin Lucas
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Proceedings Volume 11614, Design-Process-Technology Co-optimization XV; 116140R (2021) https://doi.org/10.1117/12.2584940
Event: SPIE Advanced Lithography, 2021, Online Only
Abstract
We provide background on differences between traditional and machine learning modeling. We then discuss how these differences impact the different validation needs of traditional and machine learning OPC compact models. We then provide multiple diverse examples of how machine learning OPC compact validation modeling can be appropriately validated both for modeling-specific production requirements such as model signal/contour accuracy, predictiveness, coverage and stability; and also general OPC mask synthesis requirements such as OPC/ILT stability, convergence, etc. Finally we conclude with thoughts on how machine learning modeling methods and their required validation methods are likely to evolve for future technology nodes.
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Moongyu Jeong, Marco Guajardo, Cheng-En Wu, Song-haeng Lee, Tim Fuehner, Lena Zavyalova, Li-Jin Chen, Hua Song, and Kevin Lucas "Validation of machine learning OPC compact models for advanced manufacturing", Proc. SPIE 11614, Design-Process-Technology Co-optimization XV, 116140R (22 February 2021); https://doi.org/10.1117/12.2584940
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KEYWORDS
Optical proximity correction
Machine learning
Data modeling
Manufacturing
Critical dimension metrology
Photomasks
Semiconductors
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