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Optimizing DFM scores by using a genetic evolution algorithm
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Paper 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.

Paper Details

Date Published: 20 March 2019
PDF: 7 pages
Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620J (20 March 2019); doi: 10.1117/12.2515094
Show Author Affiliations
Uwe Paul Schroeder, GLOBALFOUNDRIES Inc. (United States)
Ahmed Shalabi, GLOBALFOUNDRIES Inc. (United States)
Janam Bakshi, GLOBALFOUNDRIES Inc. (United States)
Mohamed Ismail, GLOBALFOUNDRIES Inc. (United States)
Ahmed Mounir Elsemary, GLOBALFOUNDRIES Inc. (United States)


Published in SPIE Proceedings Vol. 10962:
Design-Process-Technology Co-optimization for Manufacturability XIII
Jason P. Cain, Editor(s)

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