Design for manufacturability (DFM) physical verification using machine learning

Lynn T. N. Wang; Klaus-Peter Johnsen; Ivan Tanev; Fadi Batarseh; Chang Su; Pouya Rezaeifakhr; Uwe Paul Schroeder

doi:10.1117/12.2658646

28 April 2023 Design for manufacturability (DFM) physical verification using machine learning

Lynn T. N. Wang, Klaus-Peter Johnsen, Ivan Tanev, Fadi Batarseh, Chang Su, Pouya Rezaeifakhr, Uwe Paul Schroeder

Proceedings Volume 12495, DTCO and Computational Patterning II; 124951G (2023) https://doi.org/10.1117/12.2658646
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

Design for Manufacturability (DFM) physical verification checks using supervised Machine Learning (ML) are developed and optimized to identify via-metal enclosure weak points to prevent via opens caused by line-end shortening post-retargeting. Various methods for generating feature vectors and neural network architectures are evaluated for optimizing training time and ML model quality. Techniques include applying PCA to image-based density vectors generated from layout clips to identify the principle components or using localized layout features directly for model training. Results show that for a sample size of 300k vias, the image-based density vectors versus localized layout feature vectors achieve similar correlation coefficients of 0.95 and normalized RMSE of 0.11, with a training time of 10+ hours versus 1+ minute, respectively.

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Citation Download Citation

Lynn T. N. Wang, Klaus-Peter Johnsen, Ivan Tanev, Fadi Batarseh, Chang Su, Pouya Rezaeifakhr, and Uwe Paul Schroeder "Design for manufacturability (DFM) physical verification using machine learning", Proc. SPIE 12495, DTCO and Computational Patterning II, 124951G (28 April 2023); https://doi.org/10.1117/12.2658646

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