A fuzzy deep predictive analytics approach for enhancing cycle time range estimation precision in wafer fabrication

Abstract

The cycle time of a wafer lot refers to the time that the wafer lot has experienced from its input to output. Predicting the cycle time of each wafer lot is a crucial task for a wafer fabrication factory (wafer fab), but existing prediction methods cannot achieve 100% accuracy. Therefore, if the range of the cycle time can be estimated, it will be of great reference value. For this purpose, this research proposes a fuzzy deep predictive analytics approach. In the proposed methodology, first, decision variables related to the cycle time of a wafer lot are inputted into a deep neural network to predict the cycle time. Then, the parameters of the deep neural network are fuzzified according to an incremental fuzzification mechanism to estimate the range of the cycle time. Compared with existing methods, the proposed methodology fuzzifies more network parameters to further tighten the ranges of fuzzy cycle time forecasts. Experimental results showed that the proposed methodology improved the estimation precision in terms of the average range by approximately 80%.