- 著者
-
津田 英隆
白井 英大
寺邊 正大
橋本 和夫
篠原 歩
- 出版者
- 一般社団法人 電気学会
- 雑誌
- 電気学会論文誌D(産業応用部門誌) (ISSN:09136339)
- 巻号頁・発行日
- vol.129, no.12, pp.1201-1211, 2009-12-01 (Released:2009-12-01)
- 参考文献数
- 11
- 被引用文献数
-
4
5
The conventional semiconductor yield analysis is a hypothesis verification process, which heavily depends on engineers' knowledge. Data mining methodology, on the other hand, is a hypothesis discovery process that is free from this constraint. This paper proposes a data mining method for semiconductor yield analysis, which consists of the following two phases: discovering hypothetical failure causes by regression tree analysis and verifying the hypotheses by visualizing the measured data based on engineers' knowledge. It is shown, through experiment under the real environment, that the proposed method detects hypothetical failure causes, which were considered practically impossible to detect, and that yield improvement is achieved by taking preventive actions based on the detected failure causes.