Applying machine learning to pattern analysis for automated in-design layout optimization

التفاصيل البيبلوغرافية
العنوان: Applying machine learning to pattern analysis for automated in-design layout optimization
المؤلفون: Jason Sweis, Piyush Pathak, Moutaz Fakhry, Jason P. Cain, Frank E. Gennari, Ya-Chieh Lai
المصدر: Design-Process-Technology Co-optimization for Manufacturability XII.
بيانات النشر: SPIE, 2018.
سنة النشر: 2018
مصطلحات موضوعية: Framingham Risk Score, Process (engineering), Computer science, business.industry, Rank (computer programming), Pattern analysis, Integrated circuit, Machine learning, computer.software_genre, Pattern selection, law.invention, law, Pattern matching, Artificial intelligence, Physical design, business, computer
الوصف: Building on previous work for cataloging unique topological patterns in an integrated circuit physical design, a new process is defined in which a risk scoring methodology is used to rank patterns based on manufacturing risk. Patterns with high risk are then mapped to functionally equivalent patterns with lower risk. The higher risk patterns are then replaced in the design with their lower risk equivalents. The pattern selection and replacement is fully automated and suitable for use for full-chip designs. Results from 14nm product designs show that the approach can identify and replace risk patterns with quantifiable positive impact on the risk score distribution after replacement.
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::57cf9c5710c64943318d5850a99f8c56
https://doi.org/10.1117/12.2299492
رقم الأكسشن: edsair.doi...........57cf9c5710c64943318d5850a99f8c56
قاعدة البيانات: OpenAIRE