A classifying neural approach with principal component analysis for estimating job cycle time in a wafer fabrication factory

This study presents a classifying neural approach with principal component analysis (PCA) for estimating the cycle time of a job in a wafer fabrication factory, which is a critical task to the wafer fabrication factory. In the proposed methodology, PCA constructs a series of linear combinations of the original variables to form a new variable. Subsequently, a self-organization map (SOM) is used to classify jobs. For each category, an extreme learning machine (ELM) is constructed to estimate the cycle times of jobs. For evaluating the effectiveness of the proposed methodology, production simulation is also applied in this study to generate some test data.