Data mining for cycle time key factor identification and prediction in semiconductor manufacturing Conference Paper uri icon


  • 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 na├»ve 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.

publication date

  • January 1, 2009