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.

© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

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 (Presented at SPIE Advanced Lithography + Patterning: March 02, 2023; Published: 28 April 2023); https://doi.org/10.1117/12.2657156.