Interpretable Virtual Metrology-Driven Adaptive dEWMA Control for Nonlinear Semiconductor Process Optimization

Precise process control is essential in semiconductor manufacturing to maintain product quality and reduce variability. This paper proposes an adaptive Run-to-Run (R2R) control algorithm that integrates a virtual metrology model with interpretable machine learning techniques. The algorithm combines Accumulated Local Effects (ALE) and Shapley Additive Explanations (SHAP) to guide parameter adjustments in a double Exponentially Weighted Moving Average (dEWMA) controller. ALE gradients are utilized to refine adjustment directions and magnitudes, while SHAP values prioritize critical variables, enabling dynamic and precise control of nonlinear processes across batch runs. Comparative studies with traditional EWMA and dEWMA controllers highlight the proposed method's superior prediction accuracy and control performance, providing a robust and intelligent solution for advanced semiconductor process optimization.