Light field microscopy is an effective and accurate method for surface metrology, and Fourier light field microscopy improves spatial resolution of light field data through novel system configuration. Window-based stereo matching estimates depth from light field data through cost volumes constructed through pixel intensity and gradients. In practical stereo-matching-based reconstruction from light-field data, performance is impeded by limited pixels per sub-aperture images, degraded radiometric quality and the low dimensionality of traditional depth cues. To address this issue, this paper presents a photometrically robust spatial-frequency hybrid stereo matching method for surface measurement based on Fourier light field microscopy. Weighted average monogenic phase maps are calculated from original sub-aperture images through multi-scale monogenic filtering. Spatial-frequency hybrid cost volume is constructed by weighted combination of pixel intensity, gradients and weighted average local phase. Experimental results with a Fourier light field microscope demonstrate considerable enhancement of accuracy and robustness of the proposed method compared to traditional intensity-based stereo matching.

Microlens arrays play a crucial role in modern optical systems, such as light-field imaging and laser beam homogenization. However, fabricating microlens arrays on brittle materials remains challenging. Profile replication grinding offers an efficient, high-consistency, and cost-effective solution for machining microlens arrays on brittle materials, where machining accuracy primarily depends on the profile accuracy of the profile replication grinding wheel (PRGW). In ultra-precision envelope truing of the PRGW using an arc-shaped truing wheel, both the initial profile error of the truing wheel and profile deviations induced by its differential wear significantly degrade the profile accuracy of the PRGW. To address this, an ultra-precision truing method incorporating compensation for the truing wheel's initial and differential-wear-induced profile error is proposed in this study. The initial profile of the truing wheel is first back-calculated from the profile of the PRGW. Subsequently, the profile error caused by differential wear is predicted based on the theory of aggressiveness. The relative motion path between the truing wheel and the PRGW is adjusted to simultaneously compensate for both error sources. Experimental validation demonstrates that a single compensation cycle reduces the profile error of the PRGW to a peak-to-valley (PV) value of 62.3 nm and a root-mean-square (RMS) value of 11.2 nm, representing reductions of 82.5% and 88.9%, respectively. Additionally, the fabricated microlenses with average surface roughness (Sa) less than 5.01 nm are achieved using the PRGW trued by the proposed method, confirming its applicability for ultra-precision grinding of microlens arrays on brittle materials.

Absolute testing separates the reference surface error from the test surface error in Fizeau interferometry. To resolve high-spatial-frequency components in absolute surface errors at pixel-level resolution, a pixelated modeling approach is required to separate form errors. Conventional pixelated absolute testing, however, suffers from computational bottlenecks due to large-scale data processing. To address this limitation, we propose a linear-complexity pixelated (LCP) method for absolute testing through iterative processes. It establishes an absolute testing model by rotating and translating the test surface. The reference surface's form error is then computed with linear complexity via iteration. Simulations compare the proposed method with two alternative absolute testing methods. The results demonstrate significantly reduced computational complexity with high resolution at the pixel level. Experimental validation using flat and spherical reference mirrors confirms the method's capability to decouple surface errors containing mid-to-high-spatial-frequency components, including mounting-induced edge artifacts and localized defect signatures, demonstrating its superior absolute testing performance compared with the Zernike-fitting method.

The fatigue life of titanium alloys is closely related to multiple surface-quality parameters such as the surface morphology and residual stress after grinding. In addition, the random distribution of grinding scratches causes difficulties in quantitative characterization (numerically characterize all key features), which further complicates fatigue-life prediction. To tackle this problem, in this study, in this study, the YOLO algorithm is used to segment and characterize grinding scratches, and a multi-parameter hybrid method is proposed for predicting the fatigue life of TC4 titanium alloys subjected to belt grinding. This algorithm accurately segments the grinding scratches (loss = 1.8, accuracy = 0.78) and extracts features such as area ratio, perimeter ratio, curvature radius and scratch depth for comprehensively characterizing the surface and cross-sectional morphologies. The influence of belt-grinding speed on the surface properties is analyzed. An increase in the speed gradually increases the area ratio (from 0.01 to 0.24) and aspect ratio (from 0.43 to 0.60) and decreases the perimeter ratio (from 0.7 to 0.28) and fractal dimension (from 2.17 to 1.96). The scratch depth stabilizes at approximately 17 mm, while the curvature radius remains nearly constant. By combining morphological surface/cross-section characteristics and residual stresses, a multi-parameter hybrid model for predicting the fatigue-crack initiation and propagation life is established based on the fracture-mechanics theory. Experimental results reveal that the prediction error of the model is less than 20%, demonstrating its high prediction accuracy.

Surface wettability control is pivotal for numerous engineering applications, including self-cleaning materials, fluid drag reduction, and water harvesting. However, conventional bio-inspired surfaces often suffer from limitations in functionality, controllability, and adaptability. Addressing these challenges, this study investigates industrially prevalent polypropylene-based carbon fiber reinforced polymers. The individual and synergistic effects of surface microstructure and chemical functional groups on wettability were systematically examined. Experimental results indicate that the contact angle nonlinearly decreases with increasing edge-to-spacing ratio of the surface microstructures. Triangular prism structures demonstrated a broader wettability modulation range compared to cylindrical and square prisms. Furthermore, plasma-induced grafting of amino and hydroxyl groups significantly enhanced surface hydrophilicity, while the introduction of methyl groups effectively promoted hydrophobicity. The inherent wettability is primarily governed by surface chemistry through the regulation of surface energy, whereas the microscale topography further fine-tunes it by altering the solid-liquid contact area. By integrating experimental data with simulations, an artificial intelligence model was developed to accurately predict the water contact angle, utilizing surface energy and microstructure dimensions as key inputs. Validation experiments confirmed the model's high predictive accuracy, with an error of less than 10%. This intelligent design framework offers a novel strategy for the on-demand regulation of surface wettability in industrial applications.

Diamond turning is a mainstream technology for fabricating microstructured surfaces with high form fidelity. However, traditional systems utilizing either the slow slide servo (SSS) or fast tool servo (FTS) are constrained by inherent tradeoffs between the stroke capacity and control bandwidth of a single servo axis, restricting achievable machining performance. To address these limitations, this technical brief proposes a novel discrete Fourier transform-based parallel tool servo (DFT-PTS) diamond turning process, enabling coordinated control within a dual-stage feed drive system. In the proposed process, the initial tool trajectory is decomposed into low- and high-frequency components using the DFT and its inverse, with the low-frequency trajectory assigned to the SSS axis and the high-frequency trajectory to the FTS axis. A frequency response data-based simulation procedure is developed to determine the optimal cutoff frequency for trajectory decomposition. Experimental results on a three-axis ultra-precision machine tool equipped with a customized FTS axis show that the proposed DFT-PTS process significantly improves tracking performance. When applied to turning composite microlens arrays, the DFT-PTS process achieves a 49% reduction in peak-to-valley form error and a 52% reduction in root-mean-square form error compared with conventional geometry-based trajectory decomposition methods. These results confirm that the proposed method can simultaneously improve machining accuracy and efficiency, offering strong potential for industrial applications in ultra-precision manufacturing of microstructured optical surfaces.

Coherence scanning interferometry (CSI) is a high-precision optical method for areal surface topography measurement. However, when integrated into machine tools for on-machine measurement (OMM), its accuracy is severely compromised by environmental vibrations. This paper proposes an anti-vibration surface reconstruction method specifically designed for on-machine CSI. First, a forward imaging model of CSI under vibration is established to investigate the influence mechanism of different vibration frequencies on the measurement results. Based on this model, the proposed algorithm decomposes external vibrations into high-frequency and low-frequency components. High-frequency vibrations are suppressed using an optimized least-squares filter, while low-frequency phase errors are corrected via an inverse phase-compensation scheme. Consequently, this hardware-free method avoids iterative algorithms, ensuring high computational efficiency. The efficacy of the approach was validated using a CSI system integrated into a single-point diamond turning machine to enable OMM. Both simulations and experimental results demonstrate that the proposed method improves the measurement accuracy, enabling the precise roughness measurement of ultra-smooth surfaces in on-machine environments.

Owing to its ability to overcome the inherent tradeoff between stroke and bandwidth, the fast-slow coordinated tool servo (FSCTS) has emerged for diamond turning of complex functional optical surfaces with large depth. However, in addition to the intrinsic hysteresis and resonance associated with fast tool servo (FTS) subsystems, the FSCTS architecture introduces additional challenges, such as the phase deviation and trajectory assignment, between the fast and slow tool servo (STS) subsystems. To address all these limitations, this article proposes a synchronous high-bandwidth composite control (SHCC) scheme for the FSCTS. A novel output-feedback-based damping control strategy is developed through rigorous analysis of the system dynamics model, which can circumvent the inherent order limitation in conventional output-feedback damping controllers and thus enable effective resonance suppression of high-order FTS subsystems. On this basis, a zero-phase feedforward controller is developed to compensate the phase deviations between the FTS and STS subsystems for ensuring synchronous motion. Furthermore, an integrated optimization framework for the SHCC parameters is formulated under practical design constraints, including the closed-loop stability, stability margins, and actuator saturation, to achieve optimal tracking performance. Comparative diamond turning experiments demonstrate that the FSCTS equipped with the proposed SHCC scheme achieves superior machining accuracy and efficiency.

With the rapid advancement of precision measurement technology, interferometers face challenges in nanoscale surface shape measurement due to time-varying support deformations from clamping, which are hard to separate with conventional methods. To address this, this paper proposes a wavefront aberration decoupling method based on Zernike least squares estimation. The method constructs a multi-angle rotation model comprising four aberration components and employs an iterative algorithm to solve for their time-varying coefficients, thereby effectively characterizing dynamic clamping behavior. Quantitative evaluation is achieved by combining decoupling evaluation metrics such as standard deviation surface shape. To validate the effectiveness of the method, simulation experiments were conducted for the four main components of wavefront aberration and misalignment errors, and physical experiments were performed on an interferometric measurement platform. Simulation results show that measurement repeatability aberration and misalignment errors have the most significant impact on decoupling performance, with correlation coefficients of 0.3473 and 0.0136 nm/px, respectively. In comparison, the other three components maintain decoupling metrics below 3% of the root mean square (RMS) of the intrinsic surface shape aberration. Physical experiments, based on 12 rotation angles with a training-validation data split, demonstrate that the RMS of the standard deviation surface shape can be reduced from a baseline of approximately 1.12 nm to about 0.53 nm using the proposed iterative decoupling method, corresponding to an accuracy improvement of roughly 53%. These results confirm that the method can separate time-varying aberrations with high precision, effectively improving the accuracy and reliability of surface shape measurements.

Achieving flexible pressure sensors that simultaneously combine ultra-high sensitivity, ultra-broad detection range, and low detection limit remains a major challenge because of the intrinsic trade-off between signal sensitivity and working span. Here, we demonstrate a femtosecond laser engraving approach that directly constructs programmable hybrid micro/nanostructures on metallic electrodes in a single, mask-free step. This top-down laser patterning process produces dual-scale micro/nanostructures, significantly expanding the interfacial coupling region and enhancing sensor performance. The resulting iontronic pressure sensors exhibit extreme capabilities, characterized by a maximum sensitivity reaching 6 414 kPa ^−1 , a wide sensing range up to 800 kPa, a low detection threshold of 3.4 Pa, a rapid signal response (∼30 ms), and excellent cycling stability. For demonstration, the sensors were integrated into a wearable multi-octave glove system, and their robustness in multifunctional pressure recognition was validated. This work highlights the use of the femtosecond laser-based approach as an efficient and scalable manufacturing method for tunable, hybrid micro/nanostructures, opening new opportunities for advanced electronic skins, intelligent robotics, and human–machine interfaces.

Tool servo diamond turning is a promising technique for machining complex-shaped optics, but its application to curved microlens arrays (MLAs) encounters significant challenges, particularly in achieving homogeneous surface quality. These challenges stem from the conflict between the single spiral tool path, based on a polar coordinate system, and the periodic structural features of the curved MLAs. To address these challenges, this paper proposes a novel ultra-precision cutting process: the translation-rotation positioning stage (TRPS)-assisted tool servo diamond turning process. This process integrates an auxiliary TRPS onto the rotary axis of the ultra-precision machine tool, creating a machining system controlled by a master-slave coordinated strategy. The TRPS assists in repositioning the center of each microlens unit to the rotational center of the machine tool's rotary axis before each cut, ensuring that each lens is machined with an individual true spiral tool path. A calibration strategy is proposed to define the positional relationship between the TRPS and the machine tool, and a tool path generation algorithm is developed to precisely guide the diamond turning tool during material removal. Experimental validation on a commercial ultra-precision lathe equipped with a self-developed TRPS confirms the effectiveness of the proposed process in achieving high-precision and high-homogeneity MLAs on curved substrates. These results highlight the significant potential of the TRPS-assisted approach for industrial applications.

Hardware-level image processing requires optoelectronic devices with programmable response, simple structure, and direct optical to electrical conversion. Two-dimensional semiconductors are attractive because of their strong light-matter interaction and tunable interfaces. Yet in symmetric metal-semiconductor-metal (MSM) devices, opposite built-in fiels at the two contacts generate photocurrents with opposite polarity, thereby suppressing the net photovoltaic output needed for direct image encoding. Conventional barrier control strategies, including dissimilar electrodes, asymmetric geometries, and external gate fields, can break this symmetry; however, they often increase fabrication complexity or require continuous biasing. Here, we use localized femtosecond laser irradiation to reconfigure symmetric Au/MoTe2/Au devices. Irradiation below 20 mJ cm-2 creates a Te vacancy rich MoTe2-x region at one contact without an obvious phase transition, lowers the effective Schottky barrier, and selectively suppresses the local photocurrent. Accordingly, the engineered device enables dual wavelength image sensing and reconstruction, with structural similarity index (SSIM) improved by up to 32 fold and peak signal to noise ratio (PSNR) increased from 3 to 22 dB. These results establish femtosecond laser contact engineering as an effective route toward programmable two-dimensional optoelectronic hardware.

Microlens array elements, which are composed of periodically arranged microlenses on curved substrates, are crucial optical components to modulate light distribution in lithography and semiconductors. Their surface topography, such as shape and distribution of microlens, affect wavefront aberration of optical system. Interferometers have high accuracy, but small measurement range in single subaperture. Subaperture stitching is used to expand measurement range. Due to accumulated errors, efficiency, and coupling errors, current stitching algorithms stitch subapertures of hundreds of orders at most, which limits the measurement range of stitching. This study first investigates multiple affecting factors of stitching accumulated error to determine better subapertures layout, then proposes a stitching algorithm, named pose errors decoupling under global constraints. It inherently features high efficiency and accuracy, capable of stitching subapertures of thousands of orders. Pose error function is constructed from all valid relative pose constraints. Coupling errors are eliminated by innovatively decoupling pose error into angle error and displacement error. Accumulated errors are simultaneously eliminated by adding a few global pose constraints to error functions. By successively minimizing the two decoupled error functions, high-accuracy subaperture poses are solved. Thousands of subapertures are stitched in simulation and experiments, results show that accuracy of proposed algorithm reaches nanometer level, and errors of stitching surfaces are at the same level as instrument’s measurement errors, expanding stitching measurement range by more than ten times.

Optical microstructured lenses are essential components in aerospace, automotive electronics, and other important industries. However, achieving sub-micrometer form accuracy via ultra-precision grinding remains a significant challenge due to the high hardness and brittleness of materials such as WC-Co carbide. This study proposes a systematic grinding strategy premised on an analysis of material removal characteristics. An optimized tool path planning method is developed based on a mathematical contact model of the grinding wheel, alongside a comprehensive index for evaluating surface accuracy. By integrating an improved grinding wear error compensation algorithm, the machining process achieves a peak-to-valley surface form error (PV) below 200 nm and an areal surface roughness (Sa) under 15 nm. These results offer practical insights for advancing the industrial application of WC-Co carbide microstructured optical components.

To meet the stringent requirements for controlling assembly errors in advanced optical systems, ultra-precision alignment turning becomes the popular method. It can. ensure that the axis of symmetry of the mount coincides with the optical axis of the lens. An alignment turning system with slow slide servo can perform three-axis synchronized motions to machine the mount edge and flange surfaces without the adjustment process of the mounted lens. However, to ensure the performance of the optical system, not only must the mechanical axis of the lens mount be aligned with the optical axis, but the dimensions of the lens mount must also be precisely controlled. To address these challenges, this paper proposes an ultraprecision alignment turning method based on slow slide servo. A calibration method is proposed to precisely align the multi-tool coordinate systems with the probe coordinate systems, enabling the effective fusion of measurement data. A tool path generation method is presented for machining the mount edge and flange surfaces. It can generate the tool path with high precision and efficiency. Finally, both on-machine measurements and offline centration measurements are conducted, demonstrating that the alignment accuracy of the proposed method reaches 1.5 & micro;m.

The scaling of transistors is approaching its physical limits, making the future direction of transistor development a topic of global significance. Low-dimensional materials (LDMs), which exhibit superior properties compared to bulk materials, are emerging as a driving force for transformative advancements across various industries. What do LDMs signify for the future of transistors? Where do the challenges and opportunities lie? This perspective concludes with an overview of the transistor development roadmap, highlighting key technological milestones for LDMs, and proposes three pathways for integrating LDMs into future transistors across near-, mid-, and long-term horizons.

ABSTRACT Machine vision enables artificial intelligence and edge computing, yet its efficiency is hindered by energy‐intensive data transfer between sensing and processing units. Two‐dimensional (2D) optoelectronic neuromorphic devices provide an appealing route toward in‐sensor vision, but existing approaches typically rely on high operating power, complex multi‐terminal architectures, or elaborate fabrication, limiting scalability and practicality. Here, we demonstrate a simple two‐terminal MoS 2 metal‐semiconductor‐metal (MSM) photodetector that realizes reconfigurable and non‐volatile photovoltaic responses through photo‐assisted and electric‐field‐directed programming of native sulfur‐vacancy‐related defects, without intentional defect‐inducing pretreatment or additional functional layers. Under simultaneous 650 nm laser illumination and voltage‐pulse programming, seven stable short‐circuit photocurrent states with reversible magnitude and polarity are deterministically programmed, a functionality unattainable under optical or electrical stimulation alone. Spatially resolved measurements indicate asymmetric contact modulation after photo‐electric programming, which is supported by local photocurrent mapping, low‐temperature PL defect‐related emissions at ∼1.81 and ∼1.83 eV, KPFM surface‐potential measurements, and TEM/FFT lattice‐order analysis. Leveraging these programmable photoresponses, hardware‐measured device outputs support proof‐of‐concept motion detection, edge extraction with 95% Structural Similarity Index (SSIM) and 22 dB Peak Signal‐to‐Noise Ratio (PSNR), and front‐end preprocessing for handwritten digit recognition with accuracies exceeding 97%, establishing a defect‐engineered pathway toward energy‐efficient in‐sensor machine vision hardware.

Coherence scanning interferometry (CSI) with low-magnification objectives enables large field-of-view measurements with high axial resolution, but suffers from limited lateral resolution, leading to the loss of critical mid-spatial-frequency topography details. This paper proposes a spatial frequency response (SFR) based topography details restoration method for CSI in the frequency domain. The SFR is calculated from the calibrated three-dimensional transfer function and the corresponding imaging model. A frequency-dependent compensation factor derived from the SFR is then applied to the measured spectrum to recover the attenuated components. A noise reference surface is introduced to moderate the compensation at frequencies where the measured signal approaches the noise background, thereby preventing noise-driven artefacts. Simulations and experiments on a self-developed CSI system show improved agreement of compensated 20 & times; results with measurements from higher resolution reference instruments within the effective band, while maintaining stable behavior at higher frequencies.