Near-infrared (NIR) photon detection and object recognition are crucial technologies for all-weather target identification. Conventional NIR detection systems which rely on photodetectors and von Neumann computing algorithms have problems such as energy inefficiency. Artificial sensory neurons based on infrared-sensitive volatile memristors offer a promising way.

In a study published in Advanced Materials, a team led by Dr. WANG Jiahong from the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences developed an artificial sensory neuron based on a vanadium carbide/oxide (V₂C/V₂O_5-x) heterostructure via topochemical conversion, enabling multi-color near-infrared response and high-precision object recognition in complex scenarios.

Researchers engineered a two-dimensional V₂C/V₂O_5-x heterostructure with a natural fusion interface through a precisely controlled mild-oxidation topochemical conversion of V₂CT_x. This unique integration of metallic V₂C and dielectric vacancy-enriched V₂O_5-x granted the heterostructure NIR responsivity and threshold-type volatile resistance switching (RS) ability.

The V₂C/V₂O_5-x memristor demonstrated robust volatile capability with low coefficients of variation of merely 1.62% and 1.7% for the set and reset voltages, respectively. Its threshold voltage could be effectively modulated by power density and wavelength of NIR light. The correlation between wavelength and threshold firing voltage was consistent with photoelectric response, showing tunable photoelectric control of the V₂C/V₂O_5-x memristor via photonic parameter modulation.

"Our photoelectric programmability enables multi-color infrared discrimination through characteristic threshold voltage signatures, and the distinct wavelength responses can be encoded in the artificial sensory neuron for near-infrared object recognition," said Dr. WANG. 

Based on the multi-color NIR modulable RS characteristics and the YOLOv7 algorithm model, an artificial neural network architecture achieved average recognition accuracies of 89.6% for cars and 85.9% for persons on the FLIR dataset.

The study presents a promising memristor-based neuromorphic system that significantly enhances the efficiency and accuracy in object detection and recognition, which paves the way for advancements in autonomous systems, robotics, and intelligent environments.