DocumentCode :
1347462
Title :
Discovering Correlated Parameters in Semiconductor Manufacturing Processes: A Data Mining Approach
Author :
Casali, Alain ; Ernst, Christian
Author_Institution :
Lab. d´´Inf. Fondamentale de Marseille, Aix Marseille Univ., Aix-en-Provence, France
Volume :
25
Issue :
1
fYear :
2012
Firstpage :
118
Lastpage :
127
Abstract :
Data mining tools are nowadays becoming more and more popular in the semiconductor manufacturing industry, and especially in yield-oriented enhancement techniques. This is because conventional approaches fail to extract hidden relationships between numerous complex process control parameters. In order to highlight correlations between such parameters, we propose in this paper a complete knowledge discovery in databases (KDD) model. The mining heart of the model uses a new method derived from association rules programming, and is based on two concepts: decision correlation rules and contingency vectors. The first concept results from a cross fertilization between correlation and decision rules. It enables relevant links to be highlighted between sets of values of a relation and the values of sets of targets belonging to the same relation. Decision correlation rules are built on the twofold basis of the chi-squared measure and of the support of the extracted values. Due to the very nature of the problem, levelwise algorithms only allow extraction of results with long execution times and huge memory occupation. To offset these two problems, we propose an algorithm based both on the lectic order and contingency vectors, an alternate representation of contingency tables. This algorithm is the basis of our KDD model software, called MineCor. An overall presentation of its other functions, of some significant experimental results, and of associated performances are provided and discussed.
Keywords :
data mining; decision making; production engineering computing; semiconductor industry; statistical analysis; KDD model software; MineCor; association rule programming; chi-squared measure; contingency vectors; cross fertilization; data mining; decision correlation rules; knowledge discovery in databases; levelwise algorithms; memory occupation; semiconductor manufacturing industry; yield oriented enhancement technique; Association rules; Correlation; Lattices; Manufacturing; Process control; Semiconductor device measurement; ${rm Chi}^{2}$ correlation statistic; data mining; decision rule; semiconductor manufacturing;
fLanguage :
English
Journal_Title :
Semiconductor Manufacturing, IEEE Transactions on
Publisher :
ieee
ISSN :
0894-6507
Type :
jour
DOI :
10.1109/TSM.2011.2171375
Filename :
6042343
Link To Document :
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