Data mining for cycle time key factor identification and prediction in semiconductor manufacturing

Y. Meidan, B. Lerner, M. Hassoun, G. Rabinowitz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

We suggest a data-driven methodology to identify key factors of the cycle time (CT) in a semiconductor manufacturing plant and to predict its value. We first extract a data set from a simulated fab and describe each operation in the set using 182 features (factors). Then, we apply conditional mutual information maximization for feature selection and the selective naïve Bayesian classifier for further selection and CT prediction. Prediction accuracy of 72.6% is achieved by employing no more than 20 features. Similar results are obtained by neural networks and the C5.0 decision tree.

Original languageEnglish
Title of host publicationProceedings of the 13th IFAC Symposium on Information Control Problems in Manufacturing, INCOM'09
PublisherIFAC Secretariat
Pages217-222
Number of pages6
Edition4 PART 1
ISBN (Print)9783902661432
DOIs
StatePublished - 1 Jan 2009

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number4 PART 1
Volume42
ISSN (Print)1474-6670

Keywords

  • Industrial plant control
  • Machine learning
  • Naïve Bayesian classifier
  • Probabilistic models

ASJC Scopus subject areas

  • Control and Systems Engineering

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