Double EWMA controller using neural network-based tuning algorithm for MIMO non-squared systems

The double exponentially weighted moving average (dEWMA) control method is a popular algorithm for adjusting a process from run to run in semiconductor manufacturing. For MIMO non-squared statistic systems, the singular value decomposition (SVD) method is used for decoupling and the SVD-based dEWMA control scheme is treated as a MIMO extension of dEWMA control design. To enhance the performance and robustness of the linear system in the presence of ramp disturbances and white noises, the neural network-based adaptive algorithm is used to automatically tune the dEWMA controller parameters. Under the specified input patterns, the early stop criterion for the training-validation neural networks, and the stability constraints added in the tuning mechanism, the simulations show that the proposed control technique can effectively improve the means and standard deviations of the process outputs.