Data-Driven Feedforward Learning With Force Ripple Compensation for Wafer Stages: A Variable-Gain Robust Approach

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 4; pp. 1594 - 1608
• Main Authors: Song, Fazhi, Liu, Yang, Jin, Wen, Tan, Jiubin, He, Wei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Control methods; Convergence; Data driven; Feedforward control; Feedforward systems; force ripple; Integrated circuits; iterative learning control (ILC); Iterative methods; Learning; Parameterization; Performance enhancement; Radio frequency; Robustness; Servomotors; Stochastic processes; Stochasticity; Task analysis; Variable gain
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3042975

To meet the increasing demand for denser integrated circuits, feedforward control plays an important role in the achievement of high servo performance of wafer stages. The preexisting feedforward control methods, however, are subject to either inflexibility to reference variations or poor robustness. In this article, these deficiencies are removed by a novel variable-gain iterative feedforward tuning (VGIFFT) method. The proposed VGIFFT method attains: 1) no involvement of any parametric model through data-driven estimation; 2) high performance regardless of reference variations through feedforward parameterization; and 3) especially high robustness against stochastic disturbance as well as against model uncertainty through a variable learning gain. What is more, the tradeoff in which preexisting methods are subject to between fast convergence and high robustness is broken through by VGIFFT. Experimental results validate the proposed method and confirm its effectiveness and enhanced performance.