An Iterative Procedure for Optimizing the Performance of the Fuzzy-Neural Job Cycle Time Estimation Approach in a Wafer Fabrication Factory

Estimating the cycle time of each job in a wafer fabrication factory is a critical task to every wafer manufacturer. In recent years, a number of hybrid approaches based on job classification (either preclassification or postclassification) for cycle time estimation have been proposed. However, the problem with these methods is that the input variables are not independent. In order to solve this problem, principal component analysis (PCA) is considered useful. In this study, a classifying fuzzy-neural approach, based on the combination of PCA, fuzzy c-means (FCM), and back propagation network (BPN), is proposed to estimate the cycle time of a job in a wafer fabrication factory. Since job classification is an important part of the proposed methodology, a new index is proposed to assess the validity of the classification of jobs. The empirical relationship between the value and the estimation performance is also found. Finally, an iterative process is employed to deal with the outliers and to optimize the overall estimation performance. A real case is used to evaluate the effectiveness of the proposed methodology. Based on the experimental results, the estimation accuracy of the proposed methodology was significantly better than those of the existing approaches.