A fuzzy back propagation network ensemble with example classification for lot output time prediction in a wafer fab

Abstract

Lot output time prediction is a critical task to a wafer fabrication plant (wafer fab). To further enhance the accuracy of wafer lot output time prediction, the concept of clustering is applied to Chen’s fuzzy back propagation network (FBPN) approach in this study by pre-classifying wafer lots before predicting their output times with several FBPNs that have the same topology. Each wafer lot category has a corresponding FBPN that is applied to predict the output times of all lots belonging to the category. In choosing the learning examples of each category, whether a wafer lot can be unambiguously classified or not and the accuracy of predicting the output time of the lot are simultaneously taken into account. To validate the effectiveness of the proposed methodology and to make comparison with some existing approaches, the actual data in a wafer fab were collected. According to experimental results, the prediction accuracy of the proposed methodology was significantly better than those of some existing approaches in most cases by achieving a 19–52% (and an average of 38%) reduction in the root-mean-square-error (RMSE). On the other hand, compared with the fuzzy c-means (FCM)-BPN-ensemble approach, the performance of the proposed methodology in the efficiency respect was indeed improved.