An intelligent hybrid system for wafer lot output time prediction

Abstract

Lot output time prediction is a critical task to a wafer fab (fabrication plant). To further enhance the accuracy of wafer lot output time prediction, an intelligent hybrid system is constructed in this study. Firstly, the concept of input classification is applied to Chen’s fuzzy back propagation network (FBPN) approach in this study by pre-classifying wafer lots with the k-means (kM) classifier before predicting the output times with FBPN. Examples belonging to different categories are then learned with different FBPNs but with the same topology. Secondly, the future release plan of the fab, which is influential but has been ignored in traditional approaches, is also incorporated in the intelligent hybrid system. To evaluate the effectiveness of the proposed methodology, production simulation has been applied in this study to generate test examples. According to experimental results, the prediction accuracy of the intelligent hybrid system was significantly better than those of six approaches: BPN, case-based reasoning (CBR), FBPN, look-ahead FBPN, evolving fuzzy rules (EFR), and kM-FBPN without look-ahead in most cases by achieving a 17–47% (and an average of 34%) reduction in the root-mean-squared-error (RMSE) over the comparison basis – BPN.