Predicting Wafer-Lot Output Time With a Hybrid FCM–FBPN Approach

Abstract

Output-time prediction is a critical task to a wafer fab. To further enhance the accuracy of wafer-lot output-time prediction, the concept of input classification is applied to Chen's fuzzy backpropagation network (FBPN) approach in this paper by preclassifying wafer lots with the fuzzy c-means (FCM) classifier before predicting the output times. In this way, similar wafer lots are clustered in the same category. The data of wafer lots of different categories are then learned with different FBPNs but with the same topology. After learning, these FBPNs form an FBPN ensemble that can be applied in predicting the output time of a new lot. The output of the FBPN ensemble determines the cycle/output time forecast and is obtained by aggregating the outputs of the component FBPNs. Production simulation is applied in this paper to generate test data. According to experimental results, the prediction accuracy of the hybrid FCM-FBPN approach was significantly better than those of many existing approaches.