Neuron-Inspired Time-of-Flight Sensing via Spike-Timing-Dependent Plasticity of Artificial Synapses

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 (approximate to 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.

Automated summary

The proposed resistive time-of-flight sensor mimics the biological process of spike-timing-dependent plasticity to measure depth information in an analog domain, achieving accurate 3D imaging and classification. This system opens up new possibilities for energy-efficient neuromorphic vision engineering in various fields such as LiDAR, automotive, medical imaging, and augmented/virtual reality.