Analysis methodology for semiconductor yield by data mining

Tsuda Hidetaka, Hidehiro Shirai, Masahiro Terabe, Kazuo Hashimoto, Ayumi Shinohara

研究成果: Article査読

5 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)1201-1211+9
ジャーナルieej transactions on industry applications
129
12
DOI
出版ステータスPublished - 2009
外部発表はい

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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