Lithography tool improvement at productivity and performance with data analysis and machine learning

Yosuke Takarada; Douglas Shelton; Tsuneari Fukada; Shosi Katayama; Ken-Ichiro Mori; Seiya Miura

doi:10.1117/12.2583784

22 February 2021 Lithography tool improvement at productivity and performance with data analysis and machine learning

Yosuke Takarada, Douglas Shelton, Tsuneari Fukada, Shosi Katayama, Ken-Ichiro Mori, Seiya Miura

Proceedings Volume 11613, Optical Microlithography XXXIV; 1161308 (2021) https://doi.org/10.1117/12.2583784
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

Semiconductor manufacturing equipment must maintain high productivity and provide high-yield processing and Canon has developing high-reliability exposure tools that have demonstrated high-uptime and performance stability in production. As global emergency epidemic restrictions limit the travel of expert engineers, customer service becomes more challenging and alternative methods of support are being developed to help customers meet their production roadmaps. To help control performance, lithography tools have sophisticated logging systems that can monitor every movement in the tool and we studied a novel Artificial Intelligence system that utilizes big logging data to help improve exposure tool uptime, productivity and performance related to yield. One goal of our study is to minimize exposure tool downtime by monitoring and reacting to tool status. For this purpose we are applying machine learning to develop abnormality detection or prediction models with automated recovery procedures for each abnormality. We will report on Auto-Fault-Tree-Analysis (FTA) models being constructed to evaluate large volumes of design and trouble information to help minimize downtime. Another study goal is to improve lithography tool performance by monitoring and reacting to factors including overlay accuracy and CD uniformity that can strongly affect device yield. Outputs of this analysis include simulation and optimization of equipment performance, and virtual metrology. This paper reports on the system we are developing to help increase the uptime, productivity and imaging performance of Canon semiconductor lithography tools. The system is designed to monitor the operating state of lithography tools and apply automated recovery and optimization actions identified through machine learning.

Conference Presentation

Citation Download Citation

Yosuke Takarada, Douglas Shelton, Tsuneari Fukada, Shosi Katayama, Ken-Ichiro Mori, and Seiya Miura "Lithography tool improvement at productivity and performance with data analysis and machine learning", Proc. SPIE 11613, Optical Microlithography XXXIV, 1161308 (22 February 2021); https://doi.org/10.1117/12.2583784

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