Optimization of servo accuracy of Y axis of dicing saw based on iterative learning control

Abstract The dicing saw is a critical equipment in the semiconductor industry, where the servo performance of each axis affects the dicing accuracy. Due to the primary motion direction of the Y-axis, the servo accuracy required is particularly high. This paper proposes a Variable Forgetting Factor Fuzzy Iterative Learning Control (VFF-FILC) with a Tracking Differentiator (TD), which can optimize the motor position loop of the Y-axis in the Israeli ADT-8230 dual-axis grinding wheel dicing saw. By combining fuzzy control and iterative learning control (ILC), this strategy overcomes the limitations of traditional PID controllers. The VFF-FILC reduces overshoot and settling time while improves tracking performance by adaptively adjusting the learning rate of the ILC algorithm based on the system's tracking inaccuracy. TD improves parameter uncertainty, robustness and tracking accuracy. To verify the superiority of our proposed design, we compare it with three traditional controllers and conduct Anti-interference experiments. The results of the study show that the proposed method is more responsive, more robust and has less tracking error than traditional methods.