Accurate pattern transfer in wafer scanners necessitates the wafer stage and the reticle stage executing a coordinated motion with the synchronization error in terms of nanometers. In an attempt to cope with this challenging issue, a cross-coupling iterative learning control (ILC) with two inputs and two outputs is proposed and then decomposed into two ILC with the same convergence condition, a master–slave ILC for the reticle stage and an independent ILC for the wafer stage. To handle the inevitable stochastic disturbance, which inhibits the achievable ILC performance, an adaptive gain is involved in the proposed method for the sake of accelerated convergence as well as enhanced robustness. It remains constant or is decreased adaptively along the iteration axis, depending on the proportion that the stochastic term accounts for in the error signal. Moreover, a phase leader is chosen as the learning filter and tuned along with the initial learning gain by a frequency-domain approach. Experimental comparisons with existing close yet distinctive approaches highlight the effectiveness and superiority of the proposed method.

This paper aims to solve a closed-loop identification problem for the space-periodic force ripple in permanent-magnet linear synchronous motor (PMLSM) systems. Conventional identification schemes use the overall error signal to update estimates. However, the error caused by mechanical vibration and measurement noise could affect and even deteriorate the identification performance. In this paper, a novel iterative learning identification method that utilizes the partial but most pertinent information in the error signal is proposed to identify the force ripple. First, the effective error signal caused by the reference trajectory and the force ripple are extracted by projecting the overall error signal to a subspace. The subspace is spanned by some basis functions selected on the basis of the physical model of the PMLSM and the sinusoidal model of the force ripple. The time delay of the PMLSM system is compensated in these basis functions. Then, a norm-optimal approach is proposed to design the learning gain. The monotonic convergence of the iterative learning identification is further analyzed. Numerical simulation and experiments are provided to validate the proposed method and confirm its feasibility and effectiveness in force ripple identification, as well as its compensation.

The feedforward controller plays an important role in the achievement of high servo performance of wafer scanning. In this paper, a novel data-driven feedforward tuning method is developed in the presence of noise. Three distinguished features make it different from the existing methods: 1) high extrapolation capability to tasks; 2) low requirement on the system model; and especially 3) high noise tolerant capability. These superiorities are achieved by a high-order iterative feedforward tuning algorithm based on instrumental variables. It utilizes error data from all past iterations via an integrator in the learning law, yet without the need of the plant model or the sensitivity function. Furthermore, ${H}_{2}$ optimization with specified convergence speed constraint is proposed to design the learning gain. Connections and differences between the proposed algorithm and the existing ones are discussed finally. Experimental results validate the proposed method and confirm its effectiveness and superiority.

Accurate pattern transfer, coupled with stringent requirements for throughput and quality in lithography systems, necessitates the wafer scanner executing an aggressive motion with the tracking error in terms of nanometers. This underscores the need for advanced control technologies. The aim of this paper is to identify motion control issues in wafer scanners taking the accuracy requirement as well as the mechatronic dynamics into account, and then investigate the control methods for plants with diverse dynamics. Motion control aspects that will be discussed include: i) setpoint generator that creates smooth motion profiles through trajectory planning and input shaping, ii) multi-DOF decoupling control with static and dynamic design, iii) single DOF motion control utilizing the combination of linear/nonlinear feedback and model/data-based feedforward, and iv) multi-stage coordination control for the synchronized motion between wafer stage (WS) and reticle stage (RS), short-stroke module (SSM) and long-stroke module (LSM), as well as LSM and balance mass (BM). The control challenges associated with these aspects mainly involve: 1) cross-talk behaviors among motion DOFs, 2) flexible modes in the mechatronic dynamics, 3) model uncertainties, 4) stochastic and non-stochastic disturbances/noise, and 5) inherent design tradeoffs like Bode’s sensitivity integral. Both long-known and next-generation motion control methods are investigated and discussed with appropriate insightful comments.

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.

This article proposes the dynamic event-triggered and self-triggered protocols to achieve the prescribed-time consensus of a class of nonlinear multi-agent systems with a trade-off performance. Firstly, a dynamic event-triggered control protocol and a time-varying parameter are jointly designed to accomplish the prescribed-time consensus. Secondly, a dynamic self-triggered control protocol is further designed to avoid continuous monitoring of the system states. The designed control protocols both can ensure the avoidance of the Zeno phenomenon and allow for dynamic adjustment between the specified time and the inter-event time. Specially, the minimum inter-event time can be designed as an arbitrarily large bounded constant when every agent is an integrator system. Finally, an example system is utilized to demonstrate the effectiveness of the designed algorithms.