Training data selection and optimization for EUV lithography deep learning models

Abdalaziz Awad; Philipp Brendel; Bappaditya Dey; Sandip Halder; Andreas Erdmann

doi:10.1117/12.2658411

30 April 2023 Training data selection and optimization for EUV lithography deep learning models

Abdalaziz Awad, Philipp Brendel, Bappaditya Dey, Sandip Halder, Andreas Erdmann

Proceedings Volume 12495, DTCO and Computational Patterning II; 124951I (2023) https://doi.org/10.1117/12.2658411
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

Numerous recent approaches have proven the efficacy of deep learning as a fast and efficient surrogate for various lithography simulation use cases. However, a drawback of such approaches is the requirement of large amounts of data, which is often difficult to obtain at advanced nodes. Different means to alleviate the data demands of deep learning models have been devised, such as transfer learning from different technology nodes and active data selection. Active data selection techniques tend to require large amounts of data to optimize and select. In our previous work, we devised a more efficient implementation of transfer learning and detailed numerous applications for EUV lithography. In this work, we expand on the data efficiency enhancements with domain knowledge-based data selection and the use of alternative data generated by different modeling approaches.

Conference Presentation

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Abdalaziz Awad, Philipp Brendel, Bappaditya Dey, Sandip Halder, and Andreas Erdmann "Training data selection and optimization for EUV lithography deep learning models ", Proc. SPIE 12495, DTCO and Computational Patterning II, 124951I (30 April 2023); https://doi.org/10.1117/12.2658411

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