Due to the fast growth of the autonomous vehicles industry, there is a growing need for distance sensing systems. Among such systems, the light detection and ranging (LiDAR) system is very attractive due to their wide detection range, good spatial resolution, and high precision. For various LiDAR systems, detectors that have single photon counting (SPC) abilities are becoming increasingly popular as they have high sensitivity and can achieve long detection range. However, many publications about LiDAR only focus on the data processing and the algorithms, without sufficient considerations on the sensor-design stage. Only few studies investigated the sensor's performance in various LiDAR applications. To assist the future improvement and application of SPC LiDAR, this paper provides a new perspective by reviewing the optical sensors and the related circuits architectures in LiDAR systems, focusing on automotive applications. The principles, architectures, emerging techniques, research challenges and future directions are discussed.

With the growing importance of single-photon-counting (SPC) techniques, researchers are now designing high-performance systems based on single-photon avalanche diodes (SPADs). SPADs with high performances and low cost allow the popularity of SPC-based systems for medical and industrial applications. However, few efforts were put into the design optimization of SPADs due to limited calibrated models of the SPAD itself and its related circuits. This paper provides a perspective on improving SPAD-based system design by reviewing the development of SPAD models. First, important SPAD principles such as photon detection probability (PDP), dark count rate (DCR), afterpulsing probability (AP), and timing jitter (TJ) are discussed. Then a comprehensive discussion of various SPAD models focusing on each of the parameters is provided. Finally, important research challenges regarding the development of more advanced SPAD models are summarized, followed by the outlook for the future development of SPAD models and emerging SPAD modeling methods.

Time-gating of single-photon avalanche diodes (SPADs) was commonly used as a method to reduce dark noise in biomedical imaging applications where photon events are correlated with a reference clock. Time-gating was also used to obtain timing information of photon events by shifting the gate windows applied to a SPAD array. However, in this approach, fine timing resolution comes at the cost of a lengthened measurement time due to the large number of counts required for each shift. As a solution, we present a multi-time-gated SPAD array that simultaneously applies shifted gate windows to an array of SPADs, which has the potential to reduce the measurement time compared to a single time gate window. Compared to similar works, this design has fully integrated the multi-gate generation using shared circuitry which also functions as a coarse time-to-digital converter. The proposed array, fabricated in the TSMC 65 nm standard CMOS process, achieved a median dark count rate (DCR) of 37 kHz, 4.37 ns gate widths, 550 ps timing resolution, and a peak photon detection probability (PDP) of 42.9% at 420 nm, all at a 0.8 V excess bias.

Single-photon avalanche diodes (SPADs) enable ultrasensitive photodetection while being compatible to cost-effective standard CMOS technology. However, accurate characterization of their photon detection probability (PDP) remains underexplored, limiting the optimization of SPAD performance in standard CMOS processes. This work presents an efficient and robust PDP modeling approach that accounts for key process-dependent and experimental nonidealities. The model considers effects of interlayer dielectric (ILD), intermetal dielectric (IMD), and passivation layers, capturing their impact on optical transmission and PDP. To address experimental limitations, the simulated PDP is calibrated using measured transmission spectra of bandpass filters (BPFs). A Monte Carlo (MC) method further optimizes the PDP model with the consideration of process variations, enabling improved agreement with measurements. The proposed model achieves a mean absolute error (MAE) of 2.74% over the spectrum from 400 to 660 nm and an absolute difference of 0.44% at peak PDP at 420 nm when compared to the measured PDP results.

The investigation of neural circuit dynamics faces a fundamental challenge: existing tools cannot simultaneously achieve cellular resolution, millimeter-depth penetration, and compatibility with freely behaving subjects. Electrophysiology offers temporal precision at depth, while advanced microscopy provides superb resolution, but is physically constrained to superficial layers or head-fixed preparations. In this review, we propose that implantable photonic devices are emerging as the critical solution to address this challenge. We first critically examine the evolution of electrophysiology and microscopic imaging, establishing their inherent trade-offs. We then detail how integrated photonic probes, leveraging semiconductor innovations like single-photon avalanche diode (SPAD) arrays and μ LEDs, enable optical sensing and manipulation deep within the brain of behaving animals. By establishing frameworks to compare important performance and synthesizing the latest research, we provide analysis of this transformative shift. Finally, we outline the multidisciplinary challenges in scaling, thermal management, data processing, and biocompatibility, which must be overcome to realize the full potential of implantable photonics as a new paradigm for closed-loop neuroscience and clinical translation.