Flexure-based scanning stages driven by piezoelectric (PZT) actuator with large stroke, nanoscale precision, and high bandwidth are quite appealing for developing an advanced multiphoton polymerization 3-D nanolithography system. The motivation of this article is to develop a nanopositioning system, which can simultaneously achieve large stroke, high bandwidth, and nanoscale precision to ensure its machining size, efficiency, and accuracy without traditional step-by-step splicing operations. First, anXYnanopositioning stage with millimeter-scale workspace and nanoscale positioning accuracy is designed. Besides, the natural frequency modeling of the nanopositioning stage is conducted by resorting to compliance analysis based on the matrix method, which is validated by a finite-element analysis (FEA). Moreover, linear active disturbance rejection controller (LADRC) is demonstrated that it can be equivalent to a PID controller filtered using a second-order low-pass filter, which theoretically verifies the effectiveness of controlling the nanopositioning stage. In order to complete LADRC’s successful implementation for nanopositioning stage, a novel quantitative one-parameter-tuning method of LADRC is proposed. Finally, a series of trajectory tracking experiments has been carried out to verify the effectiveness and superiority of the proposed nanopositioning stage. The experimental results verify that the large-stroke compliant nanopositioning system has the capability to achieve millimeter stroke, which has reached 1.035 mm$\times \,\,1.035$mm, while the average tracking error is kept within ±100 nm, and the closed-loop bandwidth is achieved up to 32 Hz.Note to Practitioners—With the purpose to effectively improve the performance in terms of stroke, speed, and accuracy for the piezoelectric (PZT)-actuated scanning system, a completely decoupled nanopositioning stage with millimeter-scale travel range is developed and linear active disturbance rejection controller (LADRC) strategy with a novel quantitative one-parameter-tuning method of strategy is proposed. Experimental results indicate that the compliant nanopositioning system has the capability to achieve millimeter stroke, which has reached 1.035 mm$\times \,\,1.035$mm, while the average tracking error of dynamic tracking control of the nanopositioning stage is kept within ±100 nm, and the closed-loop bandwidth is achieved up to 32 Hz. Therefore, the developed nanopositioning system has satisfactory performance for fulfilling advanced large-size multiphoton polymerization 3-D nanolithography task without step-by-step splicing operations. In summary, the potential applications of the proposed large-stroke nanopositioning strategy will be promising.

A template matching (TM) algorithm has been widely employed in a visual inspection process of waferlevel flip-chip packaging, but the structures of traditional TM algorithms are always direct feedforward, which leads to the difficulty in achieving fast speed and high accuracy at the same time. The motivation of this article is to combine the ability to enable the chip visual measurement running in a fast-speed and high-accuracy manner. First, a novel selffeedback intelligent template matching (SFI-TM) structure is proposed, which can enable the intermediate information in the matching process to be fully utilized. The intelligent speed and resolution regulation rules are combined to construct the SFI-TM algorithm. Then, the reliability and robustness of the SFI-TM algorithm are theoretically analyzed to make sure it works in a stable manner. Finally, a series of practical chip alignment visual inspection experiments, including parameter testing, comparing experiments with the other five proposed visual detection algorithms, and robustness testing, are carried out in detail, respectively. The experimental results indicate that the SFI-TM algorithm can achieve the highest average measurement accuracy (1.08 μm) with almost the same measurement speed as fast as Tiny YOLOv2, and that it can resist the brightness variation from -20 to 30 gray value, the pepper-salt noise with a density of 0.5/pixel, and the Gaussian noise with a large variance of 2.5. In addition, it has good robustness against blurring and distortion of images under the dynamic speed motion processes.

Due to the excellent advantages of high speed and high precision, fast tool servo (FTS) system driven by piezoelectric actuators has great attraction for high-quality machining of microstructural array. However, its complex hysteresis nonlinearity at high speed will greatly affect the accuracy and stability of FTS system. Therefore, a stable autoregressive moving average (SARMA) model is proposed in this paper, which aims to describe the dynamic hysteresis nonlinearity accurately. First, a long autoregressive model residual calculation method is used to determine the order of the model and test the applicability of the model. Then, according to the Lyapunov stability theory, the strict stability analysis of the autoregressive moving average (ARMA) model is carried out in theory. By introducing the relaxation factor to transform the stability condition, the Lagrange multiplier and best square approximation method are applied to enhance the performance of the traditional ARMA model. Aiming at the difficulty of displacement sensor integration in FTS closed-loop controlling system, a hysteresis-compensated direct feedforward control strategy based on the proposed SARMA model is designed. Finally, a series of high-frequency trajectory tracking and comparing experiments has been carried out successfully with the traditional Prandtl-Ishlinskii (PI) and SARMA models to verify the effectiveness and superiority of the method. All results uniformly indicate that the SARMA model is nearly 20 times higher than the traditional PI model in terms of control accuracy and linearity, while the average linearity of FTS's dynamic tracking control is kept within 0.43% (265 nm), the stroke is 280 μm, and the positioning bandwidth is achieved up to 200 Hz.

Piezoelectric ceramics(PZT)actuator has been widely used in flexure-guided nanopositioning stage because of their high resolution. However, it is quite hard to achieve high-rate precision positioning control because of the complex hysteresis nonlinearity effect of PZT actuator. Thus, an online RELM algorithm with forgetting property(FReOS-ELM) is proposed to handle this issue. Firstly, we adopt regularized extreme learning machine(RELM)to build an intelligent hysteresis model. The training of the algorithm is completed only in one step, which avoids the shortcomings of the traditional hysteresis model based on artificial neural network(ANN) that slow training speed and easy to fall into the local minimum. Then, based on the regularized on-line sequential extreme learning machine(ReOS-ELM), an on-line RELM algorithm with forgetting property(FReOS-ELM) is designed, which can avoid the computational load of ReOS-ELM in the process of adding new data for learning on-line. In the experiment, a real-time voltage signal with varying frequencies and amplitudes is adopted, and the output displacement data of the nanopositioning stage is also acquired and analyzed. The results powerfully verify that the performance of the established hysteresis model based on the proposed FReOS-ELM is satisfactory, which can be used to improve the practical positioning performance for flexure nanopositioning stage.