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20 March 2019 Optimizing DFM scores by using a genetic evolution algorithm
Uwe Paul Schroeder, Ahmed Shalabi, Janam Bakshi, Mohamed Ismail, Ahmed Mounir Elsemary
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Proceedings Volume 10962, Design-Process-Technology Co-optimization for Manufacturability XIII; 109620J (2019) https://doi.org/10.1117/12.2515094
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States
Abstract
This paper presents a methodology to optimize standard cells and other small IP for manufacturability. The optimization is based on an evolutionary machine learning algorithm. This algorithm creates variants of a starting cell by randomly selecting and moving edges, and selects the best variant based on a scoring methodology for the next set of iterations. The opportunity for such an algorithm arises from the complexity of advanced node design rules, where multiple rules compete and have to be optimized simultaneously across multiple mask layers. Doing this process manually is a lengthy and highly iterative process and most often leaves DFM opportunities on the table. The selector in the algorithm is a combination of MAS/DRC rule-based checks, and a holistic multi-layer lithographic process window metric. Specifically library standard cells can be optimized for DFM scores and printability within a very short time frame.
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Uwe Paul Schroeder, Ahmed Shalabi, Janam Bakshi, Mohamed Ismail, and Ahmed Mounir Elsemary "Optimizing DFM scores by using a genetic evolution algorithm", Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620J (20 March 2019); https://doi.org/10.1117/12.2515094
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KEYWORDS
Design for manufacturing
Evolutionary algorithms
Genetic algorithms
Logic
Machine learning
Optical lithography
Evolutionary optimization
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