Grey model based immune predictive R2R control of CMP process

Aiming at the characteristics of nonlinearity, time-varying and impossibly of in-situ measurement of chemical mechanical polishing (CMP) process, and in order to improve the Run-to-Run (R2R) control accuracy of CMP process, this paper proposes a CMP process R2R predictive controller named GIPR2R based on grey model and clonal selection algorithms. A GM (1, N) grey predictive model is constructed using the sparse data of historical batches of CMP process, which solves the difficult problem of constructing accurate mathematical model for complicated CMP process and improves the prediction accuracy. The rolling horizon optimization of predictive control is achieved using clonal selection immune algorithm, so the problem that derivative-based optimization technology is easy to fall into local optimum is solved and the control precision is improved. Simulation results illustrate that the performance of GIPR2R controller is better than that of EWMA method, and the process drifts and shifts are suppressed significantly, the variation in various runs of products is decreased, and the RMSEs of material removal rate (MRR) for different runs and different targets are reduced by 18.09% and 16.84%, respectively.