Single-photon avalanche diodes (SPADs) are advanced photodetectors capable of detecting light under extremely low-light conditions, even single photons. Their compact size, high sensitivity, ease of integration, and strong resistance to electromagnetic interference have led to their widespread use in fields such as light detection and ranging (LiDAR), fluorescence lifetime imaging, spectroscopy, and quantum communication. These applications leverage SPADs for their precision, reliability, and ability to operate in challenging environments. This paper provides a detailed overview of SPAD, including its historical development, working principles, and device structure design. It further delves into the critical parameters and underlying principles that influence the performance of SPADs during design and manufacturing, particularly with respect to photoelectric detection applications. Key parameters such as dark count rate, photon detection efficiency, and time jitter are discussed in detail. Finally, the paper focuses on the design of quenching circuits, which are essential for controlling the avalanche process in SPADs, thereby ensuring stable operation and enhancing device performance.

In order to solve the problem of low charge conversion sensitivity and blooming under strong light irradiation of small pixel inter-line transfer charge-coupled device (ITCCD), the topology of inter-line transfer CCD device, pixel anti-blooming structure, on-chip readout circuit and process technology were studied, and a high-resolution interline transfer CCD image sensor was designed and developed, with an effective array size of 1 920×1 080 yuan and a pixel size of 7.4 m× 7.4 m. The test results show that the device breaks through the design and fabrication technology of 1.0 m polysilicon gate length amplifier, and the charge conversion sensitivity of the device can reach 22.4 V/e−, and the saturation voltage output is 1 300 mV. In order to realize the anti-blooming function, the device adopts the vertical anti-blooming structure of n-type buried channel-saddle p well-n-type substrate, and the anti-blooming ability of the device is 200 times, which can meet the requirements of high-sensitivity imaging of the device in complex lighting environments.

Pulse laser ranging technology based on time of flight (ToF) is widely used to detect non-cooperative targets due to its narrow pulse width and high peak power. In a conventional ranging system based on threshold discrimination, the measurement accuracy is limited by the range walk error of the echo signal, waveform jitter, clock drift, and system delay, resulting in power fluctuation of the echo signal, measurement data exception and error. To solve these problems, in this study, a ranging accuracy compensation based on ToF is proposed using a bi-directional convolutional neural network. The method enables anomaly detection for the pulse width with the bi-directional long short-term memory network using multi-layer constraint information, and adaptive accuracy compensation through one dimensional convolutional neural network regression analysis. In the experimental demonstration, the ranging error is 0.05~0.33 m for a measurement range of 4~110 m. Compared to the polynomial fitting method, the measurement accuracy is improved by 72.5%, which verifies the effectiveness of the proposed method.

A low-smear interline transfer charge coupled device (CCD) image sensor, with an effective array size of 2 048×2 048 elements and a pixel size of 9 μm × 9 μm, was designed and developed. To achieve low-smear characteristics, a p-well was designed and fabricated to form a barrier around the vertical CCD buried channel, reducing the entry of photo generated electrons into the channel. A microlens array was designed and fabricated in the photosensitive region to converge incident light into the photosensitive region and reduce stray light entering the vertical CCD channel. A light-blocking tungsten metal cover was designed and produced for the vertical CCD outside the photosensitive area. After testing, the device dispersion reached −82 dB, which meets the normal use requirements of the device.

In space exploration and high-speed target recognition, CMOS image sensors are required to maintain wide-field imaging with full windows while enabling regions of interest to be read out at high frame rates. However, conventional CMOS image sensors with windowing functions offer only single-window random windowing capabilities. Moreover, their frame rates cannot be increased easily, thus challenging the fulfilment of development requirements for future target tracking, pattern recognition, and space star sensor systems. Hence, utilizing a process platform for a 0.13 m CMOS image sensor, we develop a CMOS image sensor with a high frame rate and resolution to support random windowing, anti-glare, and anti-radiation functions. This image sensor adopts a rolling shutter mode, with an effective pixel array size of 1024 × 1024, a spectral response range of 400~900 nm, a dynamic range of 68 dB, and multiwindow random windowing and anti-radiation capabilities. Compared with conventional CMOS image sensors, the frame rate of the random windowing CMOS image sensor developed in this study can be increased by increasing the window size, thereby enabling the capture of rapidly traversing objects while preventing blurring and reducing motion artifacts.

Based on the nonlinear evolution equation of optical field in a single microcavity, the evolution equation for cascaded dual microcavities is derived, and the characteristics of the intracavity optical field and the impact of initial pulse on the optical field are analyzed when the dispersions in the two microcavities are opposite. The theoretical analysis results indicate that the cascaded dual microcavities not only increase the degrees of freedom for parameter selection but can also output optical frequency combs with different characteristics from the two microcavities. This can significantly enhances the generation and utilization efficiency of microcavity frequency combs. By adjusting the width, amplitude, and phase of the initial pulse, the number and position of intracavity solitons can be controlled. This work is of reference significance for generation and modulation of stable optical solitons in dual microcavity structures.

To achieve highly sensitive temperature sensing, this paper proposes a micro/nano fiber temperature-sensing structure based on chip packaging. The optical field energy distribution of micro/nano fibers with different diameters and their Brillouin scattering spectra are analyzed. Based on the characteristics of various acoustic modes, the temperature sensitivity of micro/nano fibers under different acoustic wave modes are obtained using finite element simulation. The packaging structure and materials are designed and analyzed, with a chip-scale structure and PMMA chosen for packaging. The simulation shows that the maximum temperature sensitivity of the packaged micro/nano fibers is 4.83 MHz/℃, which is approximately 363% higher than that of the unwrapped fibers, demonstrating that chip packaging can effectively enhance the temperature sensitivity of micro/nano fibers.

Owing to the continuous advancement of power-system intelligence, new power systems have imposed higher requirements for intelligent power-sensing technology. The current SOI platform of the MZI-type electric-field sensor features a narrow linear operating range, low sensitivity, and other issues. Hence, in this study, we proposed using the non-resonant state of the phase response of the microring to compensate for the MZI third-order cross-modulation distortion solution. Additionally, we performed a theoretical analysis of the different structures of the microring-assisted MZI and the use of the Lumerical software for structural simulation to verify the feasibility of the improved microring. Ultimately, we proposed the combination of two types of typical electro-optic polymers to complete the preparation of the electric-field sensor. The results of input–output testing and AC response tests of the prepared electric-field sensor show that the linear range of the slit runway-type microring-assisted MZI electric-field sensor improves by 77% and that the sensitivity of the electro-optic polymer with a high electro-optic coefficient can reach up to 1.034 mV/(kV/m), which is 2.9 times higher than that afforded by an unimproved sensor. Good responses are indicated in the AC response test. Research methods, such as theoretical analysis, simulation models, and experimental tests, confirm that the optimization strategy adopted in this study effectively improves the performance of the sensor and provides a reference for improving the direction of the topological structure of SOI optical electric-field sensors.

A high-sensitivity dual-parameter sensor for strain and temperature measurement is developed based on a Sagnac loop cascaded with a fiber Bragg grating (FBG). The sensor employs a misaligned polarization-maintaining fiber (PMF) and few-mode fiber (FMF) structure. The strain and temperature sensing characteristics were systematically analyzed through experiments. The sensor design involves splicing a section of PMF to a single-mode fiber (SMF) with deliberate misalignment along the slow axis, followed by splicing the other end of the PMF to an FMF, creating a misaligned PMF-FMF structure. The ends of this structure were connected to a 3 dB optical fiber coupler, forming a Sagnac loop, which was then cascaded with the FBG to construct the sensing probe. Experimental results demonstrate that the strain sensitivity of the Sagnac loop reaches 19.14 pm/, with a temperature sensitivity of －1.5 nm/°C. Additionally, the FBG exhibits a strain sensitivity of 1 pm/, and a temperature sensitivity of 10.8 pm/°C. This sensor achieves high sensitivity for both parameters, enabling simultaneous and accurate measurement of strain and temperature.

In this study, a three-dimensional model of an atomic layer deposition reaction chamber was established using SolidWorks software. The ANSYS Fluent fluid module was used to simulate and analyze the distribution of two precursors, Si2H6 and WF6, in the reaction chamber. The simulation results indicate that the longer the pulse time, the lower is the chamber pressure, and the higher the chamber temperature, the easier it is for the precursor to achieve a uniform distribution. At a chamber temperature of 250 °C, a carrier gas flow rate of 20 sccm, chamber pressures of 20 Pa and 15 Pa for Si2H6 and WF6, respectively, and pulse times of 30 ms and 35 ms, were achieved. The mass fraction of the precursor was found to be consistent across all regions of the chamber, achieving uniform distribution. Based on simulation data, a tungsten film was prepared on a glass substrate. Through characterization using atomic force microscopy and scanning electron microscopy, the film was found to have a low surface roughness and good thickness uniformity.

We fabricated a silicon phototransistor having a polysilicon/silicon emitter structure. To evaluate the effect of the polysilicon/silicon interface on the performance of the phototransistor, we systematically studied the variations in DC current gain (hFE) and emitter contact resistance (re) by comparing different surface pretreatment methods (routine RCA cleaning or RCA cleaning + HF dip) before the low-pressure chemical vapor deposition of polysilicon and subsequent thermal annealing at different temperatures after polysilicon preparation. Experimental results demonstrated that the additional HF dip caused the current-gain curve of the phototransistor to descend, whereas a high annealing temperature (900~1100 ℃) reduced the emitter resistance and allowed the characteristic parameters to be controlled well with minimal effects on the gain peak. This study provides valuable process references for optimizing interface engineering in phototransistors.

A new type of photonic crystal fiber with three layers of rectangular air holes in the cladding was designed and studied. The optimal structure of the fiber was obtained using finite element method modeling. On this basis, the key parameters of the optical fibers at varying wavelengths were modeled and studied. The results show that the optical fiber can support 46 modes of stable transmission in the wavelength range of 1 300~1 700 nm. In this band, the effective refractive index difference between the adjacent modes of photonic crystal fibers is greater than 10−4. The purity is 98.8%, the effective mode field area exceeds 6.4 × 10−11 m2, the nonlinear coefficient is less than 2.24 × 10−3, and the maximum numerical aperture is 0.1180. Compared with the existing photonic crystal fibers, the new fiber has more transmission modes, less transmission loss, and is made from easily accessible materials, making it promising for future optical fiber communications.

The optical diffraction limit restricts microscopic imaging resolution to the half-wavelength scale. Conventional Ghost Imaging via Sparsity Constraints-based Nanoscopy (GISC-Nanoscopy), which utilizes random phase modulators to spatially disperse fluorescence signals, suffers from reduced signal-to-noise ratio (SNR) in captured images. To address the inherent SNR limitations of traditional GISC-Nanoscopy, we propose integrating super-Rayleigh speckle modulation into the system. By leveraging the faster attenuation rate of super-Rayleigh speckle statistical distributions compared to conventional negative exponential distributions, this approach concentrates signal energy more effectively and enhances spatial contrast. The optimized system generates super-Rayleigh speckles with a contrast exceeding 1. Specifically, the enhanced system demonstrates a fluorescence signal SNR improvement exceeding 5 dB, a 30% reduction in normalized mean square error (NMSE) for reconstructed images and a 3 nm improvement in single-molecule localization accuracy compared to conventional GISC-Nanoscopy.

In view of the characteristics of steel defects and the current classification methods' dependence on complex models and non-heuristic attention mechanisms, this paper proposes a multiattention-based classification method (MACM) for steel surface defect classification based on the CBAM (Convolutional Block Attention Module) hybrid attention architecture. Firstly, heuristic channel attention is achieved through gray correlation analysis to enhance the interpretability of the algorithm and overcome the impact of interlayer information loss on channel attention determination. Secondly, compact bilinear pooling is used to achieve spatial feature fusion, enhancing the capture of nonlinear complex interaction information between abstract features. Finally, the combination of channel and spatial attention is achieved based on the CBAM architecture. The experiments show that the MACM method not only performs well but is also lighter than existing state-of-the-art methods, validating the effectiveness of the MACM algorithm in improving classification accuracy and reducing model complexity.

To address the additional energy consumption, inherent low radio frequency gain, and higher multi-wavelength channel crosstalk interference faced by intensity modulation and direct detection (IMDD) optical link in analog mobile fronthaul (A-MFH), this paper proposes a phase modulation and direct detection (PMDD) optical link scheme based on the Mach-Zehnder interferometer (MZI) to replace the traditional IMDD. In this scheme, optical phase modulation (PM) instead of intensity modulation (IM) is used in the optical transmitter, and wideband, high-linearity PM-to-IM conversion is achieved in the optical receiver using an unbalanced MZI, which enables the direct detection of phase information in the optical receiver. Extensive numerical simulation results show that when 12 channels of 100-MHz 64QAM-OFDM signals in A-MFH are transmitted synchronously over 20 km of standard single mode fiber, the optical receiver sensitivity in the PMDD optical link based on MZI achieves a 4.2 dB improvement compared to the IMDD optical link. Furthermore, the frequency response roll-off effect of the system is effectively compensated by adaptive power loading to balance the achievable performance among the 12 channels.

As a digital low-light imaging device, the electron-bombarded active pixel sensor (EBAPS) has the ability to obtain a clear image under both day and night conditions. Compared with visible light and infrared imaging, its imaging system has the advantages of miniaturization, low cost, and low power consumption, making it a popular research topic in the field of imaging. Adopting a fully domestic EBAPS low-light device concept and FPGA as the main processor, an EBAPS device driving circuit, image processing and tracking circuit, and display circuit were completed. Using an optical system that satisfied the requirements of both day and night, a miniaturized handheld imaging and tracking system was built. The experimental results show that the EBAPS imaging system realizes clear imaging and good tracking effects under illuminance conditions ranging from 1 × 10-4 to 1 × 104 lx.

With the rapid development of three-dimensional imaging technology, single-photon LiDAR has become a key technology in high-sensitivity remote sensing and precision imaging. However, under long-distance scanning conditions, traditional imaging techniques often fail to provide high-quality 3D images owing to limitations in sampling rate and resolution. To accurately simulate the detection performance of this system in various scenarios, this paper proposes a method combining a physical model and a binomial sampling process to generate echo photon data, and reconstructs 3D target images under low sampling rates using an improved TVCS algorithm. Simulation and reconstruction results indicate that LiDAR systems integrating compressed sensing theory and single-photon detection technology can effectively reduce photon acquisition and shorten imaging time. Compared with traditional OMP and TVAL3 algorithms, the proposed method maintains a superior peak signal-to-noise ratio and root mean square error, even at significantly reduced sampling rates, demonstrating improved image-recovery performance. This research not only provides new methodological support for the application of single-photon LiDAR under low photon sampling conditions but also expands the application range of compressed sensing theory in practical imaging systems. By linking the measurements of SPAD detectors with optical system parameters, the method describes the imaging process using the probability distribution of photon arrivals, thereby simulating the histogram constructed from single-photon data. Based on the generated data, time-of-flight information is obtained using a time-correlated single-photon counter on single-pixel imaging, enhancing the lateral resolution of single-point detection and providing in-depth information on the situation.

Noise figure (NF) and second harmonic distortion suppression ratio (2HD) are critical metrics for evaluating the performance of microwave photonic links. The NF characterizes the degradation of the signal-to-noise ratio and is primarily influenced by laser relative intensity noise, detector shot noise, and thermal noise. The 2HD reflects the limitation of nonlinear distortion on signal integrity. This paper proposes a dual-objective optimization method based on bias point adjustment to address the performance optimization of directly modulated microwave photonic links. The mapping relationships between the device parameters and link performance are theoretically analyzed based on establishment of a transfer model for the directly modulated optical link. Experimental measurements compare the NF and 2HD under different output power levels of the directly modulated laser. The results show that within the linear operation region of the laser, reducing the output power of a 1 550 nm wavelength laser improves the NF by 2.6 dB and maintains consistent link gain but deteriorates the 2HD by 2.44 dB. The study demonstrates a significant trade-off between the NF and harmonic suppression ratio, which necessitates dynamic selection of the optimal bias point based on practical application requirements. This work provides a foundation for flexible optimization of directly modulated links in communication, radar, and sensing systems.

Roadside vehicle detection system provides critical data support for intelligent transportation systems. However, traditional vehicle detection technologies still face challenges such as high maintenance and deployment costs. The Distributed Acoustic Sensing (DAS) system has been shown to offer passive, wide-range, and high spatial resolution vehicle detection and localization, without the need for on-site sensor installation, thus providing a significant advantage in terms of ultra-low deployment costs. However, DAS-based vehicle detection systems are susceptible to environmental noise and system fading. To address this issue, this paper proposes a Multi-Scale Context Generative Adversarial Network (MCGAN), which extracts features using multi-scale dilated convolutions and integrates hierarchical information across different scales to enhance the model's denoising performance. Experimental results demonstrate that MCGAN can effectively suppresses environmental and fading noise while preserving vehicle signals, particularly excelling in detecting low-speed and small vehicle signals.

Existing image reconstruction algorithms for ghost imaging often struggle to balance reconstruction performance and broad applicability. To address this issue, we propose a snapshot ghost imaging reconstruction algorithm based on the Plug-and-Play Alternating Direction Method of Multipliers (PnP-ADMM) framework. By seamlessly integrating high-performance denoisers, the algorithm significantly enhances reconstruction quality across both low and high sampling rates. Specifically, when employing the deep convolutional neural network FFDNet as the denoiser, the method effectively combines the strengths of model-driven and learning-driven approaches, surpassing the limitations of traditional compressed sensing and deep learning methods. Experimental results show that, compared to TVNLR, which utilizes total variation and low-rank constraints, PnP-ADMM (FFDNet) achieves a PSNR improvement exceeding 2 dB and an SSIM improvement greater than 0.15, while effectively suppressing reconstruction noise and preserving intricate image details.

Automatic recognition of traffic signs is crucial for vehicle safety in autonomous driving. To improve recognition in poor lighting conditions, an enhanced NanoDet-based traffic sign recognition algorithm is proposed. This algorithm introduces an SSM module and a CBAM attention module into the backbone network of the NanoDet model to boost accuracy under adverse lighting. The weighted bidirectional feature pyramid is used to strengthen the feature extraction. To reduce the number of model parameters and increase the viewshed, deep separable convolution is used to replace the standard convolution in the AGM module. The results on the expanded CCTSDB dataset show a speed of 138.2 frames/s and an accuracy of 90.2%, improving 4.7% compared to standard model.

To address the issue of accurately extracting the amplitude of photoelectron pulses outputted by microchannel plate-based photon-counting imaging detectors, this study investigates the correlation between the synthesis-shaping method parameters and the amplitude accuracy of pulse signals. A quasi-Gaussian synthesis-shaping approach tailored for photoelectron pulse signals is implemented using deconvolution techniques and impulse response invariance methods. The simulation results demonstrate that under a white noise standard deviation of 0.1 in the input signal, the standard deviation of the extracted amplitude converges to 0.02 as the shaping width increases, thereby enhancing the accuracy of pulse amplitude extraction. Furthermore, based on an analysis of the relationship between the pulse recognition threshold, pulse miss rate, and false recognition rate, a comparative analysis of amplitude was conducted on authentic photoelectron pulses outputted by the detector. The synthetic shaping method is closer to the real amplitude distribution than the Sallen-Key shaping method and direct amplitude extraction. This is a novel approach for high-resolution photon counting imaging applications.

To address the limitations of existing YOLOv7 models in handwashing action detection, including low detection accuracy, weak environmental interference resistance, and insufficient discrimination of similar actions, this paper proposes an enhanced CCL-YOLO object detection algorithm based on improved YOLOv7. The proposed algorithm introduces three key innovations: (1) An Enhanced Axial Local Attention mechanism is incorporated to strengthen the model′s capability in capturing long-range contextual dependencies; (2) The CARAFE operator replaces conventional nearest-neighbor interpolation for upsampling, enabling more effective content-aware feature reorganization without increasing model parameters; (3) Structural optimization from SPPCSPC to SPPFCSPC improves detection accuracy by 2.9% and frame rate by 10 while maintaining equivalent receptive fields. Additionally, a lightweight adaptive decoupled detection head is designed to replace traditional coupled detection heads, achieving a 7.6% recall improvement and 2% mAP@0.5 enhancement at the cost of only 2% precision reduction. Experimental results on a custom dataset demonstrate that the improved algorithm achieves 81.2% mAP@0.5, representing a 7.2% accuracy improvement over baseline YOLOv7, with precision and recall rates increased by 2.9% and 11% respectively. The proposed method effectively meets practical requirements for real-world handwashing action detection while maintaining computational efficiency.

For the applications of food coloring sorting and medical imaging, a high line frequency and sensitivity 1 024×1-element line column readout circuit was designed for the short-wave infrared InGaAs focal plane. The input stage of the readout circuit adopts a CTIA structure with a high injection efficiency and is designed with four levels of integrated capacitance for different application scenarios, such as low and strong light; the highest gain level of integral capacitance is 10 fF. The transient response of the output stage of the unit circuit is studied, the switch size is optimized, and the high-bandwidth op amp is designed to reduce the time constant of the key node, so as to reduce the noise of the output stage op amp and improve the dynamic range. The readout circuit layout power-supply network was optimized to suppress the voltage drop caused by excessive power consumption. The readout circuit is processed by the standard CMOS 0.18 m process and is coupled with a photosensitive chip with a pixel center distance of 12.5 m. The readout circuit adopts the “integral while readout” operation mode. The results show that the power consumption of the 1 024× 1-element line focal plane is 118.8 mW, and the dynamic range is 71 dB. At 10 fF of the integral capacitor, the focal plane noise is less than 3.13 mV, and the ultra-high line frequency readout is 40 kHz.

To address the problems of large truck blind-zone scope, complex background, large variation of target scale, poor effect of existing truck blind spot detection methods, and easy-to-miss recognition, an improved YOLOv8n truck blind zone target-detection algorithm is proposed. A mixed local channel attention module is added to the backbone network to improve the local spatial feature extraction capability of the network. The feature fusion network is improved by the scale sequence feature fusion module to fuse the deep semantic information of multiple scales of the image, the triple feature encoding module was used to capture the local details of the target, and Inner-CIoU is adopted as the bezel loss function to improve border detection accuracy. The experimental results show that on the self-built vehicle–pedestrian dataset, the proposed algorithm has a 3.14% better average detection accuracy than the traditional YOLOv8n algorithm, as well as better target detection in the blind zone of trucks.

CMOS image sensor (CIS) will suffer radiation damage when it is used in radiation environment. The radiation damage effects on the CISs in space radiation or nuclear radiation environment mainly include total ionization dose effect, displacement effect, and single particle effect. At present, it is very important to evaluate the radiation damage effects on the CISs in different radiation environments through the radiation experiments of different radiation particles or rays. This paper mainly studies the total dose effect, displacement effect and single particle effect irradiation test method of the CISs from the aspects of radiation test source, experimental procedure, radiation bias condition, and radiation test requirement, and provides the test technical support for the evaluation of the CIS radiation damage and radiation hardening performance.