An intelligent mechanism for lot output time prediction and achievability evaluation in a wafer fab

Abstract

An intelligent mechanism is constructed in this study for lot output time prediction and achievability evaluation in a wafer fabrication plant (wafer fab), which are critical tasks to the wafer fab. The intelligent mechanism is composed of two parts, and has three intelligent features: example classification, artificial neural networking, and fuzzy reasoning. In the first part of the intelligent mechanism, a hybrid self-organization map (SOM) and back propagation network (BPN) is constructed to predict the output time of a wafer lot. According to experimental results, the prediction accuracy of the hybrid SOM–BPN was significantly better than those of many existing approaches. In the second part of the fuzzy system, a set of fuzzy inference rules (FIRs) are established to evaluate the achievability of an output time forecast, which is defined as the possibility that the fabrication on the wafer lot can be finished in time before the output time forecast. Achievability is as important as accuracy and efficiency, but has been ignored in traditional studies. With the proposed mechanism, both output time prediction and achievability evaluation can be concurrently accomplished.