Retargeting-aware design for manufacturability (DFM) checks using machine learning

Lynn T. N. Wang; Uwe Paul Schroeder; Punitha Selvam; Fadi Batarseh; Pouya Rezaeifakhr; Ariel de Jesus Reyes; Teodora Nicolae; Ivan Tanev; Sriram Madhavan

doi:10.1117/12.2614460

26 May 2022 Retargeting-aware design for manufacturability (DFM) checks using machine learning

Lynn T. N. Wang, Uwe Paul Schroeder, Punitha Selvam, Fadi Batarseh, Pouya Rezaeifakhr, Ariel de Jesus Reyes, Teodora Nicolae, Ivan Tanev, Sriram Madhavan

Author Affiliations +

Proceedings Volume 12052, DTCO and Computational Patterning; 1205211 (2022) https://doi.org/10.1117/12.2614460
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

Retargeting-aware Design for Manufacturability (DFM) via-metal enclosure checks are developed using supervised machine learning to identify critical weak points to aid layout fixing. The machine learning model is developed using a neutral network architecture. Seventeen localized layout features were extracted, including: side and line end via-metal enclosure, via spacing to the neighboring features, and metal coloring. The extracted features were used to form feature vectors to train and generate a machine learning-based model for predicting post-retargeting, via-metal enclosures. This method was demonstrated on 22nm layouts. Using a neural network with 2-hidden layers, the predicted via-metal enclosure versus the actual data correlate with an R2 of 0.91 and an RMSE 0.0067.

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Citation Download Citation

Lynn T. N. Wang, Uwe Paul Schroeder, Punitha Selvam, Fadi Batarseh, Pouya Rezaeifakhr, Ariel de Jesus Reyes, Teodora Nicolae, Ivan Tanev, and Sriram Madhavan "Retargeting-aware design for manufacturability (DFM) checks using machine learning", Proc. SPIE 12052, DTCO and Computational Patterning, 1205211 (26 May 2022); https://doi.org/10.1117/12.2614460

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