Post-decomposition optimizations using pattern matching and rule-based clustering for multi-patterning technology

Lynn T.-N. Wang; Sriram Madhavan

doi:10.1117/12.2297508

20 March 2018 Post-decomposition optimizations using pattern matching and rule-based clustering for multi-patterning technology

Lynn T.-N. Wang, Sriram Madhavan

Author Affiliations +

Proceedings Volume 10588, Design-Process-Technology Co-optimization for Manufacturability XII; 105880C (2018) https://doi.org/10.1117/12.2297508
Event: SPIE Advanced Lithography, 2018, San Jose, California, United States

Abstract

A pattern matching and rule-based polygon clustering methodology with DFM scoring is proposed to detect decomposition-induced manufacturability detractors and fix the layout designs prior to manufacturing. A pattern matcher scans the layout for pre-characterized patterns from a library. If a pattern were detected, rule-based clustering identifies the neighboring polygons that interact with those captured by the pattern. Then, DFM scores are computed for the possible layout fixes: the fix with the best score is applied. The proposed methodology was applied to two 20nm products with a chip area of 11 mm² on the metal 2 layer. All the hotspots were resolved. The number of DFM spacing violations decreased by 7-15%.

Conference Presentation

Citation Download Citation

Lynn T.-N. Wang and Sriram Madhavan "Post-decomposition optimizations using pattern matching and rule-based clustering for multi-patterning technology", Proc. SPIE 10588, Design-Process-Technology Co-optimization for Manufacturability XII, 105880C (20 March 2018); https://doi.org/10.1117/12.2297508

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