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A 256 x 256 single photon avalanche diode (SPAD) sensor integrated in a 3D-stacked 90nm 1P4M/40nm 1P8M process is reported for flash light detection and ranging (LIDAR) or high speed direct time of flight (ToF) 3D imaging. The sensor bottom tier is composed of a 64x64 matrix of 36.72 m pitch modular photon processing units which operate from shared 4x4 SPADs at 9.18 m pitch and 51% fill-factor. A 16 x 14-bit counter array integrates photon counts or events to compress data to 31.4 Mbps at 30 fps readout over 8 I/O operating at 100 MHz. The pixel-parallel multi-event TDC approach employs a programmable internal or external clock for 0.56 ns to 560 ns time bin resolution. In conjunction with a perpixel correlator, the power is reduced to less than 100 mW in practical daylight ranging scenarios. Examples of ranging and high speed 3D TOF applications are given. Index Terms-3-D imaging, CMOS, direct time of flight (dTOF), histogramming, image sensor, light detection and ranging (LiDAR), single photon avalanche diodes (SPADs), time-to-digital converter (TDC), TDC sharing architecture, TOF.

Imaging systems with temporal resolution play a vital role in a diverse range of scientific, industrial, and consumer applications, e.g., fluorescent lifetime imaging in microscopy and time-of-flight (ToF) depth sensing in autonomous vehicles. In recent years, single-photon avalanche diode (SPAD) arrays with picosecond timing capabilities have emerged as a key technology driving these systems forward. Here we report a high-speed 3D imaging system enabled by a state-of-the-art SPAD sensor used in a hybrid imaging mode that can perform multi-event histogramming. The hybrid imaging modality alternates between photon counting and timing frames at rates exceeding 1000 frames per second, enabling guided upscaling of depth data from a native resolution of 64 × 32 to 256 × 128 . The combination of hardware and processing allows us to demonstrate high-speed ToF 3D imaging in outdoor conditions and with low latency. The results indicate potential in a range of applications where real-time, high throughput data are necessary. One such example is improving the accuracy and speed of situational awareness in autonomous systems and robotics.

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High-dimensional entangled states are of significant interest in quantum science as they increase the information content per photon and can remain entangled in the presence of significant noise. The authors develop the analytical theory and show experimentally that the noise tolerance of high-dimensional entanglement can be significantly increased by a modest increase in the size of the Hilbert space. For example, doubling the size of a Hilbert space with a local dimension of d = 300 leads to a reduction in the threshold detector efficiencies required for entanglement certification by two orders of magnitude. This work is developed in the context of spatial entanglement in the few-photon limit, but it can easily be translated to photonic states entangled in different degrees of freedom. The authors also demonstrate that knowledge of a single parameter, the signal-to-noise ratio, precisely links measures of entanglement to a range of experimental parameters quantifying noise in a quantum communication system, enabling accurate predictions of its performance. This work serves to answer a simple question: “Is high-dimensional photonic entanglement robust to noise?” Here, the authors show that the answer is more nuanced than a simple “yes” or “no” and involves a complex interplay between the noise characteristics of the state, channel, and detection system.

High-dimensional entangled states are of significant interest in quantum science as they increase available bandwidth and can remain entangled in the presence of significant noise. We show that a system's tolerance to noise can be significantly reduced by modest increases to the size of the Hilbert space, e.g., doubling the size of a d = 300 space leads to a reduction of the necessary detector efficiencies for entanglement certification of two orders of magnitude. We also demonstrate that knowledge of a single parameter, the signal-to-noise ratio, precisely links information theoretic measures of entanglement to a range of experimental parameters quantifying noise in a quantum communication system, enabling accurate predictions of its performance.

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Single-photon–sensitive depth sensors are being increasingly used in next-generation electronics for human pose and gesture recognition. However, cost-effective sensors typically have a low spatial resolution, restricting their use to basic motion identification and simple object detection. Here, we perform a temporal to spatial mapping that drastically increases the resolution of a simple time-of-flight sensor, i.e., an initial resolution of 4 × 4 pixels to depth images of resolution 32 × 32 pixels. The output depth maps can then be used for accurate three-dimensional human pose estimation of multiple people. We develop a new explainable framework that provides intuition to how our network uses its input data and provides key information about the relevant parameters. Our work greatly expands the use cases of simple single-photon avalanche detector time-of-flight sensors and opens up promising possibilities for future super-resolution techniques applied to other types of sensors with similar data types, i.e., radar and sonar.