A single-photon avalanche diode (SPAD) is a photon-counting sensor renowned for its exceptional single-photon sensitivity. One significant feature of SPADs is their non-linear response to light, making them ideal for high-dynamic range imaging applications. In SPAD imaging, the photon detection mode, which depends on the quenching method employed, is crucial for optimizing image quality and dynamic range. This paper examines the free-running and gating imaging modes, evaluating their impacts on photon capture and saturation limits. Given that the number of incident photons follows a Poisson distribution, we introduce an innovative imaging-quenching model based on statistical mathematics. We designed and fabricated two SPAD imaging sensors using 180 nm CMOS technology. Image processing and evaluation were conducted using a mapping method. Our results show that in low-light conditions, the gating mode surpasses the free-running mode in the signal-to-noise ratio (SNR). However, the free-running mode exhibits a saturation limit of more than an order of magnitude higher than that of the gating mode, demonstrating its superior capability to handle a broader range of light intensities. This paper provides a thorough analysis of the differences between the two imaging methods, incorporating the theoretical mathematical model, circuit characteristics, and computed imaging quality.

Single-photon avalanche diodes (SPADs) are novel image sensors that record photons at extremely high sensitivity. To reduce both the required sensor area for readout circuits and the data throughput for SPAD array, in this paper, we propose a snapshot compressive sensing single-photon avalanche diode (CS-SPAD) sensor which can realize on-chip snapshot-type spatial compressive imaging in a compact form. Taking advantage of the digital counting nature of SPAD sensing, we propose to design the circuit connection between the sensing unit and the readout electronics for compressive sensing. To process the compressively sensed data, we propose a convolution neural-network-based algorithm dubbed CSSPAD-Net which could realize both high-fidelity scene reconstruction and classification. To demonstrate our method, we design and fabricate a CS-SPAD sensor chip, build a prototype imaging system, and demonstrate the proposed on-chip snapshot compressive sensing method on the MINIST dataset and real handwritten digital images, with both qualitative and quantitative results.

Random numbers play a vital role in modern information security, where quantum random number generators (QRNGs) based on single-photon avalanche diodes (SPADs) offer a promising physical platform for quantum random-number generation. However, practical SPAD QRNGs can exhibit temporal correlation arising from finite photon-event statistics and detector nonidealities. To overcome these limitations, this paper presents a high-speed, 256-channel SPAD QRNG chip, featuring a 256 × 512-pixel array integrated with a rolling-sampling time-division multiplexing architecture. By virtue of the rolling-sampling mechanism, the single-pixel sampling interval is extended to 40.96 μs, which substantially reduces the measured temporal correlation by increasing the per-pixel revisit interval while preserving aggregate throughput. Within one complete operating cycle, the chip generates 256 random bits simultaneously, achieving a maximum aggregate raw output bitrate of 3.2 Gb/s without digital post-processing. An FPGA-based test platform was constructed for experimental verification. Measurements demonstrate that under standard indoor illumination (8 W/m²), the generated raw bitstreams pass all 15 NIST SP 800–22 statistical randomness tests at 3.2 Gb/s without post-processing. Furthermore, under controlled 530 nm illumination, the measured lag-1 serial autocorrelation coefficient decreases below the 10⁻³ reference level at an incident optical power of 11.6 μW. The proposed chip offers a low-optical-power, high-throughput solution for integrated hardware security.