A Parallel Inverse-Model-Based Iterative Learning Control Method for a Master-Slave Wafer Scanner

Abstract

Tracking and synchronization accuracies are key performance indicators for advanced wafer scanners. In this paper, we propose a new Parallel Inverse-Model-based Iterative Learning Control (PIMILC) method in which the tracking and synchronization accuracies of the master-slave wafer scanners are jointly considered. In PIMILC, a parallel ILC structure is adopted and the tracking error of the wafer stage filtered by a compensation filter is fed into the reticle stage to decouple the learning system. Furthermore, an inverse-model-based learning law with robustness enhancement techniques is used to trade-off among robustness, accuracy and convergence rate. Simulation results confirm that the PIMILC method can significantly reduce the tracking and synchronization errors compared to prior work.