A 256 x 256 single-photon avalanche diode (SPAD) sensor integrated into a 3-D-stacked 90-nm 1P4M/40-nm 1P8M process is reported for flash light detection and ranging (LIDAR) or high-speed direct time-of-flight (ToF) 3-D imaging. The sensor bottom tier is composed of a 64 x 64 matrix of 36.72-μm pitch modular photon processing units which operate from shared 4 x 4 SPADs at 9.18-μm pitch and 51% fill-factor. A 16 x 14 bit counter array integrates photon counts or events to compress data to 31.4 Mb/s at 30-frame/s readout over 8 I/O operating at 100 MHz. The pixel-parallel multi-event time-to-digital converter (TDC) approach employs a programmable internal or external clock for 0.56-560-ns time bin resolution. In conjunction with a per-pixel correlator, the power is reduced to less than 100 mW in practical daylight ranging scenarios. Examples of ranging and high-speed 3-D ToF applications are given.

A 128 × 128 single-photon avalanche diode (SPAD) motion detection-triggered time-of-flight (ToF) sensor is implemented in STMicroelectronics 40 nm CMOS SPAD foundry process. The sensor combines vision and ToF ranging functions to acquire depth frames only when inter-frame intensity changes are detected. The 40 μm × 20 μm pixels integrate two 16-bit time-gated counters to acquire ToF histograms and repurpose them to compare two vision frames without the requirement for additional out-of-pixel frame memory resources. An embedded column-parallel ToF and vision processor performs on-chip vision frame comparison and binary frame output compression as well as controlling the time-resolved histogram sampling. The sensor achieves a maximum 32.5 kframes/s in vision modality and 500 frames/s in motion detection-triggered ToF over a measured 3.5 m distance with 1.5 cm accuracy. The vision function reduces the sensor power consumption by 70% over continuous ToF operation and allows the sensor to gate the ToF laser emitter to reduce the system power when no motion activity is observed.

A miniaturized 1.4 mm $\times \,\, 1.4$ mm, $128\times120$ single-photon avalanche diode (SPAD) image sensor with a five-wire interface is designed for time-resolved fluorescence microendoscopy. This is the first endoscopic chip-on-tip sensor capable of fluorescence lifetime imaging microscopy (FLIM). The sensor provides a novel, compact means to extend the photon counting dynamic range (DR) by partitioning the required bit depth between in-pixel counters and off-pixel noiseless frame summation. The sensor is implemented in STMicroelectronics 40-/90-nm 3-D-stacked backside-illuminated (BSI) CMOS process with 8- $\mu \text{m}$ pixels and 45% fill factor. The sensor capabilities are demonstrated through FLIM examples, including ex vivo human lung tissue, obtained at video rate.

This paper examines methods to best exploit the High Dynamic Range (HDR) of the single photon avalanche diode (SPAD) in a high fill-factor HDR photon counting pixel that is scalable to megapixel arrays. The proposed method combines multi-exposure HDR with temporal oversampling in-pixel. We present a silicon demonstration IC with 96 × 40 array of 8.25 µm pitch 66% fill-factor SPAD-based pixels achieving >100 dB dynamic range with 3 back-to-back exposures (short, mid, long). Each pixel sums 15 bit-planes or binary field images internally to constitute one frame providing 3.75× data compression, hence the 1k frames per second (FPS) output off-chip represents 45,000 individual field images per second on chip. Two future projections of this work are described: scaling SPAD-based image sensors to HDR 1 MPixel formats and shrinking the pixel pitch to 1–3 µm.

On-chip capacity for storing temporal photon data in direct time-of-flight (dToF) lidar sensors is limited. This has prompted the development of various partial histogram approaches to reduce the amount of data stored on-chip. The aim of this paper is to inform sensor design by providing a taxonomy of these approaches, models for evaluating their impact on system laser power and identification of additional trade-offs which must be considered. All published on-chip partial histogram lidar approaches to-date are reviewed and two main categories are established: zooming and sliding. A means of evaluating any specific configuration based on its histogram reduction ratio (HRR) is also established. To quantitatively evaluate partial histogram approaches, a model to determine the minimum number of required laser cycles is developed. Both zooming and sliding are compared to an ideal baseline using this model, in order to establish a laser power penalty benchmark for each approach. These are evaluated over a range of real-world design conditions for two contrasting designs: short-range indoor and long-range outdoor. In general, a sliding approach is found to be the most laser power-efficient for long-range outdoor applications, while a zooming approach becomes increasingly more effective under low ambient conditions. Power efficient cycle-scaled variations on the conventional zooming and sliding approaches are introduced. These are shown to consistently reduce the laser power penalty across all tested design conditions. It is also shown that a cycle-scaled sliding histogram approach can be adopted to reduce the required on-chip histogram storage capacity by half, with almost no additional laser power penalty. Finally, a qualitative discussion of zooming and sliding compares additional key design considerations such as sensitivity to motion artefacts.

Self-driving vehicles demand efficient and reliable depth-sensing technologies. Lidar, with its capability for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking ‘guided’ dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in which the exposed time window of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up to 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over twenty times, while the total laser cycles in each frame are reduced by at least six times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar.