Paper Abstract and Keywords |
Presentation |
2017-10-26 09:30
[Invited Talk]
Utilization of Big Data for Innovation in Semiconductor Memory Manufacturing
-- Comprehensive Big-Data-Based Monitoring System for Yield Analysis in Semiconductor Manufacturing -- Hiroshi Akahori (Toshiba Memory), Kouta Nakata, Ryohei Orihara, Yoshiaki Mizuoka, Kentaro Takagi (Toshiba), Kenichi Kadota, Takaharu Nishimura, Yukako Tanaka, Hidetaka Eguchi (Toshiba Memory) SDM2017-55 Link to ES Tech. Rep. Archives: SDM2017-55 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this work, we focus on yield analysis task where engineers identify the cause of failure from wafer failure map patterns and manufacturing histories. We organize yield analysis task into 3 stages, failure map pattern monitoring, failure cause identification and failure recurrence monitoring, and incorporate machine learning and data mining technologies into each stage to support engineers' work. The important point is that big data analysis enables comprehensive and long-term monitoring automation. Machine learning and data mining techniques are integrated into a real automated monitoring system with interfaces familiar to engineers to attain large yield enhancement. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Big-Data / Semiconductor / Yield Analysis / Machine Learning / Data Mining / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 260, SDM2017-55, pp. 31-33, Oct. 2017. |
Paper # |
SDM2017-55 |
Date of Issue |
2017-10-18 (SDM) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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SDM2017-55 Link to ES Tech. Rep. Archives: SDM2017-55 |
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