Article ID Journal Published Year Pages File Type
719360 IFAC Proceedings Volumes 2009 6 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Engineering Computational Mechanics
Authors
, , , ,