PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Feature Selection for trouble shooting in complex assembly lines
T. Pfingsten, D.J.L. Herrmann, T. Schnitzler, A. Feustel and B. Schölkopf
IEEE Transactions on Automation Science and Engineering Volume 4, Number 3, pp. 465-469, 2007.

Abstract

The final properties of sophisticated products can be affected by many unapparent dependencies within the manufacturing process, and the products’ integrity can often only be checked in a final measurement. Troubleshooting can therefore be very tedious if not impossible in large assembly lines. In this paper we show that Feature Selection is an efficient tool for serial-grouped lines to reveal causes for irregularities in product attributes. We compare the performance of several methods for Feature Selection on real-world problems in mass-production of semiconductor devices. Note to Practitioners— We present a data based procedure to localize flaws in large production lines: using the results of final quality inspections and information about which machines processed which batches, we are able to identify machines which cause low yield.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4026
Deposited By:Bernhard Schölkopf
Deposited On:25 February 2008