With the rapid development of sensor networks, machine vision faces the problem of storing and computing massive data. The human visual system has a very efficient information sense and computation ability, which has enlightening significance for solving the above problems in machine vision. This review aims to comprehensively summarize the latest advances in bio-inspired image sensors that can be used to improve machine-vision processing efficiency. After briefly introducing the research background, the relevant mechanisms of visual information processing in human visual systems are briefly discussed, including layer-by-layer processing, sparse coding, and neural adaptation. Subsequently, the cases and performance of image sensors corresponding to various bio-inspired mechanisms are introduced. Finally, the challenges and perspectives of implementing bio-inspired image sensors for efficient machine vision are discussed.

Structure illumination microscopy (SIM) imposes no special requirements on the fluorescent dyes used for sample labeling, yielding resolution exceeding twice the optical diffraction limit with low phototoxicity, which is therefore very favorable for dynamic observation of live samples. However, the traditional SIM algorithm is prone to artifacts due to the high signal-to-noise ratio (SNR) requirement, and existing deep-learning SIM algorithms still have the potential to improve imaging speed. Here, we introduce a deep-learning-based video-level and high-fidelity super-resolution SIM reconstruction method, termed video-level deep-learning SIM (VDL-SIM), which has an imaging speed of up to 47 frame/s, providing a favorable observing experience for users. In addition, VDL-SIM can robustly reconstruct sample details under a low-light dose, which greatly reduces the damage to the sample during imaging. Compared with existing SIM algorithms, VDL-SIM has faster imaging speed than existing deep-learning algorithms, and higher imaging fidelity at low SNR, which is more obvious for traditional algorithms. These characteristics enable VDL-SIM to be a useful video-level super-resolution imaging alternative to conventional methods in challenging imaging conditions.

Optical singularity is pivotal in nature and has attracted wide interest from many disciplines nowadays, including optical communication, quantum optics, and biomedical imaging. Visualizing vortex lines formed by phase singularities and fabricating chiral nanostructures using the evolution of vortex lines are of great significance. In this paper, we introduce a promising method based on two-photon polymerization direct laser writing (2PP-DLW) to record the morphology of vortex lines generated by tightly focused multi-vortex beams (MVBs) at the nanoscale. Due to Gouy phase, the singularities of the MVBs rotate around the optical axis and move towards each other when approaching the focal plane. The propagation dynamics of vortex lines are recorded by 2PP-DLW, which explicitly exhibits the evolution of the phase singularities. Additionally, the MVBs are employed to fabricate stable three-dimensional chiral nanostructures due to the spiral-forward property of the vortex line. Because of the obvious chiral features of the manufactured nanostructures, a strong vortical dichroism is observed when excited by the light carrying orbital angular momentum. A number of applications can be envisioned with these chiral nanostructures, such as optical sensing, chiral separation, and information storage.

We have successfully demonstrated a 1 Kb spin-orbit torque (SOT) magnetic random-access memory (MRAM) multiplexer (MUX) array with remarkable performance. The 1 Kb MUX array exhibits an in-die function yield of over 99.6%. Additionally, it provides a sufficient readout window, with a TMR/R_P_sigma% value of 21.4. Moreover, the SOT magnetic tunnel junctions (MTJs) in the array show write error rates as low as 10^?6 without any ballooning effects or back-hopping behaviors, ensuring the write stability and reliability. This array achieves write operations in 20 ns and 1.2 V for an industrial-level temperature range from ?40 to 125 °C. Overall, the demonstrated array shows competitive specifications compared to the state-of-the-art works. Our work paves the way for the industrial-scale production of SOT-MRAM, moving this technology beyond R&D and towards widespread adoption.