Combining statistical analysis and artificial neural network for classifying jobs and estimating the cycle times in wafer fabrication

Estimating the cycle time of a job is meaningful in many ways for managing a wafer fabrication factory (wafer fab). However, this estimation is not easy, due to the complexity and uncertainty of the wafer fabrication environment. Recently, a number of hybrid methods have been proposed to improve the accuracy of estimating the cycle time of a job. Most of these methods used job classification, especially pre-classification. Among these methods, several were based on post-classification and achieved even better performances. Such post-classification-based methods were improved in this study by considering the required parameter adjustment instead of the estimation error. Thus, it is possible to classify jobs into more than two categories at the same time. From the view of neurocomputing, this study established a systematic and effective procedure to divide the input examples to an artificial neural network into several parts that can be better handled by different artificial neural networks. A real case was also used to illustrate the applicability of the proposed methodology. The effectiveness of the proposed methodology over several existing methods has been confirmed by statistical tests.