Application of Genetic Algorithm to Solving Better SRAF Rules
Author:
Affiliation:
1. School of Micro-Nano Electronics, Zhejiang University,Hangzhou,China
2. Zhejiang ICsprout Semiconductor,Process Integration Department,Hangzhou,China
3. Calibre Foundry Team, Siemens EDA,Shanghai,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10365714/10366059/10366128.pdf?arnumber=10366128
Reference6 articles.
1. Lithographic comparison of assist feature design strategies
2. The performance improvement of SRAF placement rules using GA optimization
3. Sub-resolution-assist-feature placement study to dense patterns in advanced lithography process
4. Use of ILT-based mask optimization for local printability enhancement
5. Using heuristic optimization to set SRAF rules
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