A self-tuning EWMA controller utilizing artificial neural network function approximation techniques

Recent works have shown that an exponentially weighted moving average (EWMA) controller can be used on semiconductor processes to maintain process targets over extended periods for improved product quality and decreased machine downtime. Proper choice of controller parameters (EWMA weights) is critical to the performance of this system. This work examines how different process factors affect the optimal controller parameters. We show that a function mapping from the disturbance state (magnitude of linear drift and random noise) of a given process to the corresponding optimal EWMA weights can be generated, and an artificial neural network (ANN) trained to learn the mapping. A self-tuning EWMA controller is proposed which dynamically updates its controller parameters by estimating the disturbance state and using the ANN function mapping to provide updates to the controller parameters. The result is an adaptive controller which eliminates the need for an experienced engineer to tune the controller, thereby allowing it to be more easily applied to semiconductor processes