Simultaneous prediction for multiple key performance indicators in semiconductor wafer fabrication

The prediction and key factors identification for lot Cycle time (CT) and Equipment utilization (EU) which remain the Key performance indicators (KPI) are vital for multi-objective optimization in semiconductor manufacturing industry. This paper proposes a prediction methodology which predicts CT and EU simultaneously and identifies their key factors. Bayesian neural network (BNN) is used to establish the simultaneous prediction model for Multiple key performance indicators (MKPI), and Bayes theorem is key solution in model complexity controlling. The closed-loop structure is built to keep the stability of MKPI prediction model and the weight analysis method is the basis of identifying the key factors for CT and EU. Compared with Artificial neural network (ANN) and Selective naive Bayesian classifier (SNBC), the simulation results of the prediction method of BNN are proved to be more feasible and effective. The prediction accuracy of BNN has been obviously improved than ANN and SNBC.