Abstract
Cycle time reduction is crucial for semiconductor wafer fabrication to maintain competitive advantages. A proper allocation of tools among interchangeable tool sets based on the accurate forecasts for arrival rates of tool sets has significant potential for cycle time reduction. This study aims to propose a data mining approach to support the decision making for determining the tool allocation among interchangeable tool sets. We proposed a hybrid method that combines the applications of decision tree and back-propagation neural network (BPNN) to forecast the arrival rates of jobs for individual tool sets. In addition, based on the forecasts for arrival rates and other historical input variables, we built BPNN models to predict the work-in-process (WIP) levels of individual tool sets. The results from data mining suggest the tool allocation that can minimize the total WIP of interchangeable tool sets given the same throughput level, i.e. to minimize cycle time of interchangeable tool sets. The proposed approaches were validated via an empirical study in a wafer fab and the results showed practical viability of the propose approach.