Combining on-line experiment and process control methods for changes in a dynamic model

Abstract

During the past decade, a variety of ‘run-to-run’ (R2R) control schemes have been proposed and investigated extensively using various semiconductor manufacturing methods. However, such control has a problem when it is suddenly faced with a larger process change that does not satisfy the control requirements. In view of this situation, a new process control framework is proposed, which integrates response surface modelling, evolutionary operation (EVOP) and R2R control principles. The primary objective of this study is to improve dynamic model parameter prediction, enabling more effective optimized recipe calculations. The recursive least squares (RLS) algorithm is used to evaluate changes in the process parameters. If the evaluated parameter values exceed a joint parameter threshold, the recipe moves to a new optimum point. This movement, which continuously applies the design of experiment (DOE) concepts to collect process data in the experimental range, uses this data with the least squares error (LSE) method to estimate the new model parameters. Then, the renewed model applies the minimized total cost principle (the cost function structure includes an expected off-target and controllable factors adjustment) and uses the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm to obtain a recipe for the next period. Simulation studies show that the proposed system has better control performance than the traditional self-tuning controller. In the relevant chemical mechanical planarization (CMP) application of semiconductor manufacturing, one critical chip fabrication step is also used to illustrate the proposed control procedure in a dynamic process.