An intelligent process monitoring system in complex manufacturing environment

In high-tech industries, most manufacturing processes are complexly intertwined, in that manufacturers or engineers can hardly control a whole set of processes. They are only capable of looking after each individual process. They have difficulty in verifying the cause of process abnormalities from isolating special cause variability within a number of common causes, and in predicting future processes. In this paper, we present an intelligent process monitoring and prediction system (IPMS) for the semiconductor industry that has complex and dynamic manufacturing characteristics. The system is based on artificial neural networks, and is used to monitor and control a whole set of processes over a period of time. The IPMS consists of three major functions: Feature extraction, process pattern analysis, and process stability evaluation and prediction. The system has strong applicative advantages in manufacturing situations whereby control of a variety of quality variables is interrelated.