Machine learning architecture evaluation for fast and accurate weak point detection

Suraag Sunil Tellakula; Uwe Paul Schroeder; Janam Bakshi; Punitha Selvam; Fadi Batarseh; Pouya Rezaeifakhr; Sriram Madhavan

doi:10.1117/12.2613828

26 May 2022 Machine learning architecture evaluation for fast and accurate weak point detection

Suraag Sunil Tellakula, Uwe Paul Schroeder, Janam Bakshi, Punitha Selvam, Fadi Batarseh, Pouya Rezaeifakhr, Sriram Madhavan

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Proceedings Volume 12052, DTCO and Computational Patterning; 120520K (2022) https://doi.org/10.1117/12.2613828
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

This work is evaluating Machine Learning (ML) architecture options for weak point detection methods embedded in Design for Manufacturing (DFM) signoff tools. As Deep Learning based models have been released into the customer design enablement space, we are investigating the tradeoffs between model simplicity, run time and prediction accuracy. With simpler model architectures, additional options for data augmentation become available that can potentially result in better model accuracy. For example, noise introduction in the training data set can help prevent overfitting and thus results in better model accuracy.

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Suraag Sunil Tellakula, Uwe Paul Schroeder, Janam Bakshi, Punitha Selvam, Fadi Batarseh, Pouya Rezaeifakhr, and Sriram Madhavan "Machine learning architecture evaluation for fast and accurate weak point detection", Proc. SPIE 12052, DTCO and Computational Patterning, 120520K (26 May 2022); https://doi.org/10.1117/12.2613828

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