Multivariable immune predictive R2R control method for CMP process

In order to solve the R2R(run-to-run) control problem in chemical mechanical polishing (CMP) process with the features of multi-input & multi-output and difficulty of product quality online measurement, a CMP process multivariable predictive R2R controller named BSVMPR2R based on Bayes least squares support vector machine (BLS-SVM) prediction model and the clonal selection immune multi-objective receding horizon optimization algorithm are proposed.LS-SVM and Bayes evidence framework(BEF) methods are used to build the BLS-SVM prediction models of material removal rate (MRR) and within-wafer nonuniformity (WIWNU), respectively, which solve the mismatch problem of linear prediction model.The prediction errors are used to online estimate the next run disturbances and drifts, achieve feedback correction and improve the prediction model accuracy.Multivariable control problem is transformed into multi-objective optimization problem based on the two prediction models, and clonal selection immune multi-objective receding horizon optimization algorithm is used to solve the optimal control law, which improves the control precision.Simulation results illustrate that the performance of BSVMPR2R controller is superior to that of double exponential weighted moving average (dEWMA) multivariable controller, the effects of CMP process disturbances and drifts are restrained, and the RMSEs of MRR and WIWNU are reduced significantly.