A Fuzzy-Neural Approach with Collaboration Mechanisms for Semiconductor Yield Forecasting

Abstract

Yield forecasting is critical to a semiconductor manufacturing factory. To further enhance the effectiveness of semiconductor yield forecasting, a fuzzy-neural approach with collaboration mechanisms is proposed in this study. The proposed methodology is modified from Chen and Lin’s approach by incorporating two collaboration mechanisms: favoring mechanism and disfavoring mechanism. The former helps to achieve the consensus among multiple experts to avoid the missing of actual yield, while the latter shrinks the search region to increase the probability of finding out actual yield. To evaluate the effectiveness of the proposed methodology, it was applied to some real cases. According to experimental results, the proposed methodology improved both precision and accuracy of semiconductor yield forecasting by 58% and 35%, respectively.