TY - JOUR
T1 - Analysis methodology for semiconductor yield by data mining
AU - Hidetaka, Tsuda
AU - Shirai, Hidehiro
AU - Terabe, Masahiro
AU - Hashimoto, Kazuo
AU - Shinohara, Ayumi
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Data mining
KW - Hypothesis discovery
KW - Regression tree analysis
KW - Semiconductor
KW - Yield analysis
UR - http://www.scopus.com/inward/record.url?scp=75349111143&partnerID=8YFLogxK
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U2 - 10.1541/ieejias.129.1201
DO - 10.1541/ieejias.129.1201
M3 - Article
AN - SCOPUS:75349111143
VL - 129
SP - 1201-1211+9
JO - IEEJ Transactions on Industry Applications
JF - IEEJ Transactions on Industry Applications
SN - 0913-6339
IS - 12
ER -