Single-photon LiDAR (SPL), based on time-correlated single-photon counting technology, offers extremely high sensitivity. However, its detection capability is severely limited under strong sunlight. Although macropixel combined with multi-photon coincidence detection can effectively suppress background noise, optimizing their multidimensional parameter space traditionally relies on time-consuming, high-cost, and labor-intensive stepwise experimentation, which is also prone to redundancy. Therefore, there is an urgent need to develop a comprehensive and high-throughput method to simplify and optimize the macropixel configuration workflow across different environments.

To overcome these limitations, a simulation method was proposed. This method integrated two core computational models: the photon echo model, which characterized laser beam transmission and target interaction, and the signal readout model, which simulated photon-response behavior in single-photon detectors and described the operational logic of macropixel signal readout circuits. By coupling these models, the optimal macropixel configuration parameters that maximized SPL detection performance in specific environments could be efficiently obtained. Specifically, the photon echo model calculated the expected flux of signal photons returning from the target. Subsequently, the signal readout model simulated the triggering process of the detector in response to signal photons and solar background noise photons, based on Poisson statistics. It determined valid readout signals through an integrated mechanism involving programmable delayed detection windows and tunable photon-event triggering thresholds. By systematically scanning different values for detection window length and triggering threshold, the coupled simulation framework could generate photon-counting histograms for various macropixel configurations under different solar background light intensities.

Using this simulation approach, photon-counting histograms for both single pixels and macropixels under varying solar background light intensities were obtained, followed by detection performance analysis to derive signal-to-noise ratio (SNR) data for macropixels under different background conditions. The results clearly revealed the fundamental limitation of single-pixel SPL under sunlight: it was inherently incapable of effectively extracting target echo signals, with SNRs consistently below 1.0. In stark contrast, macropixel configurations (1 × 2, 2 × 2, 2 × 4) equipped with dynamically optimized coincidence detection parameters exhibited strong noise suppression and significant SNR enhancement. Crucially, the proposed method identified specific combinations of detection window timing and triggering thresholds that maximized SNR for each macropixel size under given background conditions. For instance, under an extreme solar background intensity of 70 klx, optimized parameter combinations achieved SNRs of 10.25 for 1 × 2 macropixels, 12.26 for 2 × 2 macropixels, and 17.51 for 2 × 4 macropixels (Table 2 ~ Table 4)—representing orders-of-magnitude improvements over single-pixel performance. Furthermore, photon-counting histograms of optimized macropixels consistently displayed a prominent and distinct peak, a feature entirely absent in single-pixel histograms. These results robustly demonstrated the inherent superiority of macropixel coincidence detection schemes for noise suppression. This indicated that, given known LiDAR system specifications, selected macropixel structures, and background light intensity, the proposed simulation method could rapidly and efficiently determine the precise combination of triggering threshold levels and coincidence window lengths required to achieve the optimal SNR for that specific operating point.

This study successfully implements and validates a global simulation framework for spatiotemporal coincidence detection mechanisms in macropixel signal readout within SPL systems. Its core achievement lies in determining the specific optimal combination of photon-event triggering thresholds and coincidence detection window parameters that maximize system SNR for given LiDAR system parameters, macropixel structures, and current background light intensity. This simulation method offers a viable alternative to expensive and time-consuming experimental iterations, facilitates efficient exploration of macropixels’ complex parameter space, and establishes a critical theoretical and practical foundation for optimizing and accelerating the development of macropixel-based signal readout circuits.