Artificial neural network exponentially weighted moving average controller for semiconductor processes

The linear exponentially weighted moving average (EWMA) controller has been shown to improve run-by-run process control for approximately linear processes that are subject to shifts or persistent drifts in the presence of noise. This work addresses the inability of the linear EWMA controller to adequately control processes that are poorly represented by such models. This issue is important to the success of the EWMA controller in semiconductor manufacturing where processes may be poorly approximated with linear process models. We address this issue by outlining an extension of the EWMA controller that utilizes an artificial neural network (ANN) process model. The ANN model is dynamically updated using an EWMA of the biases in the ANN output layer. Recipe generation takes place by optimizing around the dynamic ANN model. We show that this framework improves on the linear EWMA controller for controlling higher order processes. Simulations show that this controller provides stable control for higher order processes that cause the linear EWMA controller to become unstable. In addition, we suggest that this architecture is robust in the face of model error and noise. This system allows the basic property of the EWMA controller, improved control with minimal added process noise, to be extended for use in higher order semiconductor processes.