ScholarWorks@성균관대학교

초록

Over the past few decades, LiDAR technology has become a cornerstone in various fields, including autonomous vehicles, augmented reality, and robotics. Unlike traditional scanning LiDAR systems, flash LiDAR technology captures the entire field of view in a single frame, enabling faster 3D mapping with fewer optical components. To manage the large influx of photons received by the sensor, on-chip histogramming time-to-digital converters (hTDC) are widely employed, facilitating time-correlated single-photon counting techniques [1-5]. However, their physical size imposes constraints on spatial resolution. Partial histogramming techniques [1-3] or shared hTDC architectures leveraging advanced 3D stacking technologies [4,5] are introduced to shrink its size and improve spatial resolution, although they are susceptible to background light intensity [4] or present limitations in detection range and frame rates [5]. Meanwhile, conventional frame-based LiDAR sensors offer a suboptimal frame rate determined by the pixel with the worst signal-to-noise ratio (SNR) condition. An asynchronous event-driven readout scheme [6] is incorporated into a time-gated photon counting sensor with spiking neurons to support versatile operating modes and frame rates. However, essential control signals for pixel sensitivity and event rates are driven by external components, limiting the scalability. © 2025 IEEE.