Machine learning architecture evaluation for fast and accurate weak point detection

Yi Lee; Uwe Paul Schroeder; Pouya Rezaeifakhr; Lynn Wang; David Villarreal

doi:10.1117/12.2657156

28 April 2023 Machine learning architecture evaluation for fast and accurate weak point detection

Yi Lee, Uwe Paul Schroeder, Pouya Rezaeifakhr, Lynn Wang, David Villarreal

Proceedings Volume 12495, DTCO and Computational Patterning II; 1249519 (2023) https://doi.org/10.1117/12.2657156
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

This paper proposes a new methodology that can greatly accelerate Manufacturability Analysis & Scoring (MAS) deck runtime. The intention of this work is to provide a quick preview check to ensure that a new design will pass MAS signoff. Instead of running the deck on the full design, the input design is sampled down to a few random locations which are then analyzed. Furthermore, the actual MAS checks are replaced by an ML trained lookup methodology that keys off very simple design parameter like layer area density and layer perimeter density. The output of the deck is a simple PASS/FAIL statement and a range forecast for the MAS score based on a statistical assessment. We can demonstrate 4x runtime improvement while incurring minor tradeoffs for accuracy.

Conference Presentation

Citation Download Citation

Yi Lee, Uwe Paul Schroeder, Pouya Rezaeifakhr, Lynn Wang, and David Villarreal "Machine learning architecture evaluation for fast and accurate weak point detection", Proc. SPIE 12495, DTCO and Computational Patterning II, 1249519 (28 April 2023); https://doi.org/10.1117/12.2657156

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