Neural network based uniformity profile control of linear chemical-mechanical planarization

In this paper, a neural network based uniformity controller is developed for the linear chemical-mechanical planarization (CMP) process. The control law utilizes the metrology measurements of the wafer uniformity profile and tunes the pressures of different air-bearing zones on Lam linear CMP polishers. A feedforward neural network is used to self-learn the CMP process model and a direct inverse control with neural network is utilized to regulate the process to the target. Simulation and experimental results are presented to illustrate the control system performance. Compared with the results by using statistical surface response methods (SRM), the proposed control system can give more accurate uniformity profiles and more flexibility.