Metallic mesh is a transparent electromagnetic shielding film with a fine metal line structure. However, in production preparation or actual use it can develop defects that affect the optoelectronic performance. The development of in situ non-destructive testing (NDT) devices for metallic mesh requires long working distances, reflective optical path design, and miniaturization. To address the limitations of existing smartphone microscopes, which feature short working distances and inadequate transmission imaging for industrial in situ inspection, we propose a novel long-working-distance reflective smartphone microscopy (LD-RSM) system. LD-RSM comprises a 4 f optical imaging system with external optical components and a smartphone. This system uses a beam splitter to achieve reflective imaging with the illumination system and imaging system on the same side of the sample. It achieves an optical resolution of 4.92 µm and a working distance of up to 22.23 mm. Additionally, we introduce dual-prior weighted robust principal component analysis (DW-RPCA) for defect detection. This approach leverages spectral filter fusion and the Hough transform to model different defect types, which enhances the accuracy and efficiency of defect identification. Coupled with a double-threshold segmentation approach, the DW-RPCA method achieves a pixel-level defect detection accuracy ( f -value) of 0.856 and 0.848 in square and circular metallic mesh datasets, respectively. Our work shows strong potential in the field of in situ industrial product inspection.

Decoupling control is a widely employed technique used to mitigate coupling effects and bridge the gap between multiple-input multiple-output (MIMO) and single-input single-output (SISO) control. In the field of precision motion control, the cross-talk resulting from coarse decoupling poses a significant challenge to achieving high performance. Static decoupling control fails to completely decouple the MIMO system due to dynamic disparities between drives, actuators, and the flexible modes of the plant. Consequently, dynamic decoupling control methods have gained attention for their potential to enhance performance. However, existing dynamic decoupling control methods suffer from limitations such as reliance on system models, and susceptibility to disturbances and noise. In this paper, these deficiencies are addressed by 1) a data-based optimization with no involvement of model knowledge, and 2) using the augmented vector and instrumental variable to eliminate the estimation bias caused by position-dependent disturbances and measurement noise. The effectiveness and superiority of the proposed method are substantiated through numerical simulations and experiments conducted on an ultra-precision wafer stage. Note to Practitioners—This paper presents a non-iterative dynamic decoupling method, which is suitable for LTI multivariable systems with stringent precision requirements, such as the wafer stage and the atomic force microscope. In the field of precision motion control, the feedback controller plays a crucial role in the stability and robustness of the system. However, to achieve the desired performance, additional methods may be necessary, such as feedforward control method, nonlinear control method, etc. Especially for multiple-input multiple-output systems, it is essential to maintain high decoupling accuracy to avoid the cross-talk, which emerges due to a coarse decoupling. Given the practical differences in dynamic characteristics between drives and actuators, as well as non-rigid modes in the system, dynamic decoupling is recommended over static decoupling. Furthermore, it is promising to develop a data-based method that enables inevitable uncertainties between the model and the actual plant. In the dynamic decoupling method proposed in this paper, the impact of position-dependent disturbances and the measurement noise are jointly taken into consideration, which has not been previously addressed in literature. The augmented vector and instrumental variable are introduced to eliminate the adverse effects of these irrelevant data. Consequently, an unbiased estimate of the dynamic decoupling controller can be obtained. The practicality and effectiveness of the proposed method have been demonstrated. In future work, we will focus on the optimal selection of the dynamic decoupling controller under bounded disturbances and its application to redundant systems.

Confocal microscopy is an essential component of wafer defect detection systems. Wafers are raw materials used in the manufacture of semiconductor chips. The semiconductor chip manufacturing process undergoes frequent updates, which cause an increase in the number and types of defects. This leads to lengthy scanning times for large wafers, and warrants the need to enhance the throughput of optical microscopy inspections. To address this issue, we propose the use of the multi-scale residual dilated convolution attention mechanism network (MRDCAN) super-resolution reconstruction algorithm to reproduce high-resolution images from low-magnification objective lens acquired images. The algorithm introduces the attention mechanism to enhance the information richness of wafer images, introduces the multi-scale expansion convolution to expand the convolutional sensor field to eliminate artefacts to enrich the detailed information of wafer image contours, and meets the image quality requirements through the loss calculation method based on the combination of mean-square error (MSE) and structural similarity (SSIM) image evaluation indices. It is shown that the reconstruction of low-resolution wafer images using this algorithm breaks the optical diffraction limit and achieves the purpose of improving the wafer image resolution. Compared with state-of-the-art models, the proposed algorithm can achieve the best performance with an SSIM index of 94.26 percent for the reconstructed super-resolution wafer images. Our algorithm provides fresh insights into the current challenges of confocal microscopy in the field of wafer defect detectionNote to Practitioners—Shrinking semiconductor wafer sizes and increasingly complex inspection steps lead to reduced throughput of optical microscope inspection systems. Current convolutional neural network (CNN) networks cannot solve the problem of super-resolution of complex wafer images well. This seriously affects their application in practical detection. Compared with other algorithms, the super-resolution reconstruction algorithm proposed in this paper has a short training time and a multi-scale structure that effectively prevents the loss function curve from oscillating. And the reconstructed wafer image achieves obvious advantages in terms of visual effect and evaluation indices, with strong robustness to Gaussian noise. In addition, the final discussion shows that high-resolution images can be reproduced through the combination of low-magnification objective lens and deep learning super-resolution algorithm, which can simplify the steps of wafer defect detection and increase the efficiency of the whole wafer defect detection by more than 100%. This study demonstrates the potential of super-resolution confocal microscopy for wafer defect detection.

Autonomous underwater vehicles (AUVs) face significant challenges in trajectory tracking due to nonlinear dynamics, actuator faults, and environmental disturbances. To address these issues, this article proposes a novel fault-tolerant control strategy that ensures fixed-time trajectory tracking with prescribed accuracy for underactuated AUVs. The proposed approach integrates boundary functions with a constraint-handling mechanism, enabling guaranteed tracking performance within a fixed time horizon while satisfying output constraints. Unlike existing approaches, the controller does not rely on accurate system models, parameter estimation, or external observers, and avoids the computation of virtual control derivatives, resulting in reduced computational complexity. Moreover, the control scheme maintains robustness against time-varying actuator faults and environmental disturbances without auxiliary adaptation or learning mechanisms. Simulation results demonstrate the effectiveness and superior performance of the proposed approach compared with existing methods, validating its capability to maintain tracking accuracy and closed-loop stability under adverse operating conditions.

Precise measurement is the basis for the precise assembly of aeroengine rotors. However, due to the limitations of data application in the measurement process and the insufficient digitization of auxiliary assembly, the guided assembly after precise measurement heavily relies on manual analysis and operation. Moreover, due to the difficulty of fully utilizing measurement data of aeroengine rotors to guide assembly in existing assembly methods, it is hard to unify measurement and assembly processes, which limits the efficiency of aeroengine rotors assembly. According to the real-time response and high-fidelity interaction capability of the digital twin (DT) system, a coaxiality prediction model of aeroengine multistage rotors assembly and a cloud-based measurement data rapid interaction are established. Based on the large coaxiality measuring instrument, a DT system with measurement and real-time guidance for assembly is designed. Augmented reality device is used as the “sixth dimension” of the DT system to realize the digital measurement and assembly of aeroengine rotors. The experiment uses a certain type of engine three-stage rotors for measurement and assembly. The experimental results show that the large-scale coaxiality measuring instrument DT system can carry out accurate measurement and real-time assembly guidance for the engine, and achieve breakthroughs in engine assembly quality in multiple metrics, which significantly broadens the application dimension of precision measuring instruments.

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.

Representation of orientation is important in a six-degree-of-freedom grating interferometer but only a few studies have focused on this topic. Roll-pitch-yaw angles, widely used in aviation, navigation, and robotics, are now being brought to the field of multi-degree-of-freedom interferometric measurement. However, the roll-pitch-yaw angles are not the exact definitions the metrologists expected in interferometry, because they require a certain sequential order of rotations and may cause errors in describing complicated rotations. The errors increase as the tip and tilt angles of the grating increase. Therefore, a replacement based on fused angles in robotics is proposed and named “fused-like angles.” The fused-like angles are error-free, so they are more in line with the definitions in grating interferometry and more suitable for six-degree-of-freedom measurements. Fused-like angles have already been used in research on the kinematic model and decoupling algorithm of the six-degree-of-freedom grating interferometer.

Finding the optimal optoelectronic properties (zero-order optical transmittance, shielding effectiveness, and stray light uniformity) of metallic mesh is significant for its application in electromagnetic interference shielding areas. However, there are few relevant studies at present. Based on optoelectronic properties, we propose a comprehensive evaluation factor Q , which is simple in form and can be used to evaluate the mesh with different parameters in a simple and efficient way. The effectivity of Q is verified by comparing the trend of Q values with the evaluation results of the technique for order preference by similarity to ideal solution (TOPSIS). The evaluation factor Q can also be extended to evaluate the optoelectronic properties of different kinds of metallic meshes, which makes it extremely favorable for metallic mesh design and application.

In the semiconductor manufacturing industry, the dynamic model of a controlled object is usually obtained from a frequency sweeping method before motion control. However, the existing isolators cannot properly isolate the disturbance of the inertial force on the platform base during frequency sweeping (the frequency is between 0 Hz and the natural frequency). In this paper, an adjustable anti-resonance frequency controller for a dual-stage actuation semi-active vibration isolation system (DSA-SAVIS) is proposed. This system has a significant anti-resonance characteristic; that is, the vibration amplitude can drop to nearly zero at a particular frequency, which is called the anti-resonance frequency. The proposed controller is designed to add an adjustable anti-resonance frequency to fully use this unique anti-resonance characteristic. Experimental results show that the closed-loop transmissibility is less than −15 dB from 0 Hz to the initial anti-resonance frequency. Furthermore, it is less than −30 dB around an added anti-resonance frequency which can be adjusted from 0 Hz to the initial anti-resonance frequency by changing the parameters of the proposed controller. With the proposed controller, the disturbance amplitude of the payload decays from 4 to 0.5 mm/s with a reduction of 87.5% for the impulse disturbance applied to the platform base. Simultaneously, the system can adjust the anti-resonance frequency point in real time by tracking the frequency sweeping disturbances, and a good vibration isolation performance is achieved. This indicates that the DSA-SAVIS and the proposed controller can be applied in the guarantee of an ultra-low vibration environment, especially at frequency sweeping in the semiconductor manufacturing industry.

A grating interferometer, called the “optical encoder,” is a commonly used tool for precise displacement measurements. In contrast to a laser interferometer, a grating interferometer is insensitive to the air refractive index and can be easily applied to multi-degree-of-freedom measurements, which has made it an extensively researched and widely used device. Classified based on the measuring principle and optical configuration, a grating interferometer experiences three distinct stages of development: homodyne, heterodyne, and spatially separated heterodyne. Compared with the former two, the spatially separated heterodyne grating interferometer could achieve a better resolution with a feature of eliminating periodic nonlinear errors. Meanwhile, numerous structures of grating interferometers with a high optical fold factor, a large measurement range, good usability, and multi-degree-of-freedom measurements have been investigated. The development of incremental displacement measuring grating interferometers achieved in recent years is summarized in detail, and studies on error analysis of a grating interferometer are briefly introduced.

Iterative feedforward tuning (IFFT) enables high performance for motion systems that perform varying tasks without the need for system models. In this paper, IFFT is employed for a wafer stage to achieve good trajectory tracking performance and excellent disturbance compensation ability. Recently, the instrumental variable (IV) approach has been introduced into IFFT algorithms (IV-IFFT), enabling unbiased estimates for the parameters of a feedforward controller in the presence of stochastic noise. However, the estimation variances achievable with IV-IFFT are larger than zero. The aim of this paper is to develop an IFFT algorithm that enables unbiased estimates with zero asymptotic variances, which can be achieved by the simultaneous use of the Kalman filtering (KF) approach and the IV approach in IFFT, yielding the KF-IV-IFFT algorithm. The different roles of KF and IV approaches to improve the noise-tolerant capability of IFFT are also revealed. Experimental results obtained on a wafer stage confirm the practical relevance of the proposed KF-IV-IFFT algorithm.