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Discovering correlated parameters in Semiconductor Manufacturing processes: a Data Mining approach

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.
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Submitted on : Wednesday, October 17, 2012 - 5:09:27 PM
Last modification on : Monday, June 27, 2022 - 3:06:04 AM
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Christian Ernst, Alain Casali. Discovering correlated parameters in Semiconductor Manufacturing processes: a Data Mining approach. IEEE Transactions on Semiconductor Manufacturing, Institute of Electrical and Electronics Engineers, 2012, 25 (1), pp.118-127. ⟨10.1109/TSM.2011.2171375⟩. ⟨emse-00742962⟩



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