Neuron-inspired time-of-flight sensing via spike-timing-dependent plasticity of artificial synapses

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3D sensing is a primitive function that allows imaging with depth information generally achieved via the time-of-flight (ToF) principle. However, time-to-digital converters (TDCs) in conventional ToF sensors are usually bulky, complex, and exhibit large delay and power loss. To overcome these issues, a resistive time-of-flight (R-ToF) sensor that can measure the depth information in an analog domain by mimicking the biological process of spike-timing-dependent plasticity (STDP) is proposed herein. The R-ToF sensors based on integrated avalanche photodiodes (APDs) with memristive intelligent matters achieve a scan depth of up to 55 cm (89% accuracy and 2.93 cm standard deviation) and low power consumption (0.5 nJ/step) without TDCs. The in-depth computing is realized via R-ToF 3D imaging and memristive classification. This R-ToF system opens a new pathway for miniaturized and energy-efficient neuromorphic vision engineering that can be harnessed in light-detection and ranging (LiDAR), automotive vehicles, biomedical in vivo imaging, and augmented/virtual reality.

Recommended citation: Park, Minseong, Yuan Yuan, Yongmin Baek, Andrew H. Jones, Nicholas Lin, Doeon Lee, Hee Sung Lee, Sihwan Kim, Joe C. Campbell, and Kyusang Lee. “Neuron-Inspired Time-of-Flight Sensing via Spike-Timing-Dependent Plasticity of Artificial Synapses.” Advanced Intelligent Systems 4, no. 3 (2022): 2100159.