A Data-Driven EWMA-KNN Run-to-Run Controller for Drift-Dominant Processes with Application to Chemical Mechanical Planarization

This paper presents a data-driven run-to-run (R2R) controller for manufacturing processes subject to process drift. The proposed approach combines the exponentially weighted moving average (EWMA) method with the K-nearest neighbors (KNN) algorithm to determine process recipe adjustments. Control actions are derived entirely from historical process output data. In the hybrid controller, the EWMA estimator recursively updates the accumulated process drift using historical process errors and generates the corresponding recipe compensation. The KNN-based controller, in turn, identifies the K nearest neighbors in the historical feature database based on the current process error and determines the compensation action from the associated error–compensation relationships. The proposed controller was evaluated through simulations of a chemical mechanical planarization (CMP) process, with removal rate as the control objective. Under linear process drift with random white-noise disturbances, the proposed controller maintained the removal rate close to the target value, with a maximum overshoot of 4.40%, and satisfied the settling criterion from the beginning of the control process. Its performance was superior to that of the conventional EWMA controller and the standalone KNN controller. The EWMA controller exhibited several oscillations during the initial runs, with a maximum overshoot of 15.17%. Although the KNN controller satisfied the settling criterion from the beginning of the control process and produced a relatively small maximum overshoot of 3.10%, it did not consistently maintain the removal rate near the target value. Under nonlinear process drift with random disturbances, the proposed controller also maintained the process output near the target value with satisfactory stability, provided that the process drift remained within a bounded range. The controller also has a simple and intuitive implementation, which may facilitate practical industrial application.