1. Neuron‐Inspired Time‐of‐Flight Sensing via Spike‐Timing‐Dependent Plasticity of Artificial Synapses
- Author
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Minseong Park, Yuan Yuan, Yongmin Baek, Andrew H. Jones, Nicholas Lin, Doeon Lee, Hee Sung Lee, Sihwan Kim, Joe C. Campbell, and Kyusang Lee
- Subjects
intelligent matters ,LiDAR ,memristors ,neuromorphic computing ,resistive time-of-flight ,Computer engineering. Computer hardware ,TK7885-7895 ,Control engineering systems. Automatic machinery (General) ,TJ212-225 - Abstract
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.
- Published
- 2022
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