Explore process weak patterns for manufacturing friendly design

Jet Jiang; Frank Hou; Gavin Li; Marfe Ma; Kuan Hu; Chunshan Du; Qijian Wan; Zhengfang Liu; Xinyi Hu; Elven Huang; Wael ElManhawy

doi:10.1117/12.2551691

23 March 2020 Explore process weak patterns for manufacturing friendly design

Jet Jiang, Frank Hou, Gavin Li, Marfe Ma, Kuan Hu, Chunshan Du, Qijian Wan, Zhengfang Liu, Xinyi Hu, Elven Huang, Wael ElManhawy

Author Affiliations +

Proceedings Volume 11328, Design-Process-Technology Co-optimization for Manufacturability XIV; 1132812 (2020) https://doi.org/10.1117/12.2551691
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

Abstract

Silicon weak pattern exploration becomes more and more attractive for yield improvement and design robustness as these proven silicon weak patterns or hotspots directly reveals process weakness and should be avoided to occur on the chip design. At the very beginning, only a few known hotspot patterns are available as seeds to initialize the weak pattern accumulation process. Machine learning technique can be utilized to expand the weak pattern database, the data volume is critical for machine learning. Fuzzy patterns are built and more potential hotspots locations are found and sent to YE team to confirm, thus more silicon proven data is available for machine learning model training, both good patterns and bad patterns are valuable for the training data set. The trained machine learning model is then used for new hotspots prediction. The outcome from the machine learning prediction need to be validated by silicon data in the first few iterations. When a reliable machine learning model is ready for hotspots detection, designers can run hotspot prediction at the design stage. There are some techniques in training the mode and will be discussed in details in the paper.

Citation Download Citation

Jet Jiang, Frank Hou, Gavin Li, Marfe Ma, Kuan Hu, Chunshan Du, Qijian Wan, Zhengfang Liu, Xinyi Hu, Elven Huang, and Wael ElManhawy "Explore process weak patterns for manufacturing friendly design", Proc. SPIE 11328, Design-Process-Technology Co-optimization for Manufacturability XIV, 1132812 (23 March 2020); https://doi.org/10.1117/12.2551691

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