A fuzzy-neural and multiple-bucket approach for estimating lot cycle time in a wafer fab with dynamic product mix
A fuzzy-neural and multiple-bucket approach is proposed in this study for lot cycle time estimation in a wafer fab with dynamic product mix, which was seldom thoroughly investigated in the past studies. The proposed methodology is composed of two parts. In the first part, the multiple-bucket approach is applied to consider the future release plan of the wafer fab. Subsequently, the FCM-FBPN approach is applied to estimate the cycle time of every lot in the wafer fab. The buckets obtained in the first part become additional inputs to the FBPN. In this way, the fluctuation in the product mix since the release of a wafer lot can be considered in estimating the cycle time of the wafer lot. According to experimental results, the estimation accuracy of the proposed methodology was significantly better than those of many existing approaches. Other findings include that a large number of buckets were beneficial to the estimation accuracy, and might not worsen the efficiency of the proposed methodology. (C) 2008 Elsevier Ltd. All rights reserved.
bucket cycle time wafer fab dynamic product mix fuzzy back propagation network fuzzy c-means