Mingjun Ren

Achieving flexible pressure sensors that simultaneously combine ultra-high sensitivity, ultra-broad detection range, and low detection limit remains a major challenge due to 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 6414 kPa ^-1 , a wide sensing range up to 800 kPa, a low detection threshold of 3.4 Pa, rapid signal response (~30 ms), and excellent cycling stability. For demonstration, the sensors were integrated into a wearable multi-octave glove system, validating their robustness in multifunctional pressure recognition. This work highlights 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.

Coherence scanning interferometry (CSI) is widely used to measure surface microstructures and roughness. In such applications, characterizing the instrument’s topographic lateral resolution is essential for assessing the fidelity and limitations of measured topography. Using prior knowledge of the instrument’s three-dimensional transfer function, this study presents a virtual-instrument-based approach for evaluating the spatial frequency response (SFR) of CSI, which enables quantitative characterization of its topographic lateral resolution. The instrument’s three-dimensional transfer function is obtained from a standard microsphere measurement and used to construct a virtual CSI for SFR calibration. The method is applied to a commercial CSI system under different defocus conditions to evaluate the influence of defocus on the SFR characteristics. Two reference sinusoidal gratings are then measured to show close agreement between the virtual-instrument-based SFR calibration and the measured height responses.

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. Graphical Abstract

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 to the Zernike-fitting method.

Absolute testing is a key technique for achieving nanoscale measurements in Fizeau interferometry. Traditional methods use Zernike polynomials to fit the test surface form and indirectly infer the reference surface form, which can lead to error accumulation. In this article, we propose a neural absolute testing method that uses an advanced neural radiance field to directly model the reference surface form, thereby reducing error accumulation. The proposed framework includes an invariant surface branch and a variant surface branch; the sum of these two branches is expected to closely approximate the measured data under varying rotations and shifts. Consequently, a self-supervised training strategy has been developed to optimize the model parameters, eliminating the need for extensive training datasets with ground truth labels. Synthetic experiments demonstrate that the proposed method achieves superior absolute calibration accuracy compared to the traditional Zernike polynomial approach. Furthermore, real-world experiments performed using a Fizeau interferometer confirm that the proposed method produces a more robust reference surface form than the classical Zernike polynomial technique.

This article presents a multidelayed repetitive control (MDRC) scheme to achieve the ultra-precision tracking control of the fast tool servo (FTS) for diamond turning of functional surfaces. A general time-domain analysis of the FTS toolpath is conducted by using the Taylor expansion, which reveals that the maximal deviation between two adjacent toolpath cycles is approximately several hundreds of nanometers and thus indicates that the FTS toolpath presents quasi-periodic. Consequently, an extra time delay term is induced in the MDRC scheme to compensate this deviation and thus results in the enormous accuracy improvement for tracking the quasi-periodic toolpaths than the conventional RC scheme. The stability analysis of the MDRC scheme is provided based on the small gain theorem, following by the detailed design procedure. Moreover, comprehensive toolpath tracking experiments of three typical functional surfaces and machining experiments of the microlens array are carried out on a self-developed FTS system and a commercial ultra-precision lathe. Experimental results show that the proposed MDRC scheme can achieve the ultra-precision tracking for the FTS toolpath with the root-mean-square errors less than 30 nm and has potential to track quasi-periodic trajectories with the slowly varying amplitude in other ultra-precision machining and measurement fields.

Fringe projector profilometry provides incomplete and noisy measurement data for machined metal surfaces that are extensively utilized in various industrial applications. Previous methods have attempted to address this issue through high dynamic range imaging or by introducing reflectance information of extra facilities with multiple lights. However, these methods decrease measurement efficiency and complicate the measurement facility. Therefore, this paper combines these two strategies by utilizing the reflectance information from nonfringe images to guide the fusion and restoration of incomplete phase information at different exposure times, where an advanced anisotropic reflectance model is employed to obtain the reflectance information. A global neural calibration model based on a neural radiance field is proposed to bridge the phase and reflectance information. This model comprises global neural phase-to-coordinate and phase-to-light models. The global neural phase-to-coordinate model uses the neural radiance field to establish a relationship from the pixel position and the relative phase to the relative point coordinate. Besides, the global neural phase-to-light model employs the neural radiance field to describe the light distribution of the projector. The global neural calibration model transforms and propagates the reflectance information to optimize the phase information, thereby enhancing the performance of fringe projection profilometry for machined metal surfaces. Real experiments on standard ball bars demonstrate that the global neural calibration model achieves calibration accuracy of 31.89 μm. In addition, experiments on six machined metal workpieces demonstrate that the proposed method achieves measurement accuracy of 58.5 μm compared with coordinate measuring machine results.