2D Vanadium Carbide/Oxide Heterostructure-Based Artificial Sensory Neuron for Multi-Color Near-Infrared Object...

Near-infrared (NIR) photon detection and object recognition are crucial technologies for all-weather target identification in autonomous navigation, nighttime surveillance, and tactical reconnaissance. However, conventional NIR detection systems, which rely on photodetectors and von Neumann computing algorithms, are plagued by energy inefficiency and signal transmission bottlenecks. Herein, a vanadium carbide/oxide (V₂C/V₂O_5-x) heterostructure is designed and synthesized by a topochemical conversion method. The V₂C/V₂O_5-x heterostructure-based memristor exhibits stable threshold-type resistance switching (RS) behavior with low coefficient of variation in transition voltages (1.62% and 1.7%) over thousands of cycles, and maintains stable performance even after storage for 90 days. Benefiting from the NIR responsivity of V₂C and the volatile RS enabled by vacancy-enriched V₂O_5-x, devices exhibit a linear variation in threshold voltage in response to NIR light power density and wavelength. Based on the multi-color NIR modulable RS characteristics and the YOLOv7 algorithm model, an artificial neural network (ANN) architecture achieves average recognition accuracies of 89.6% for cars and 85.9% for persons on the FLIR dataset. This work reveals a heterostructure with versatile functionalities for neuromorphic devices and establishes a memristor-based ANN platform for multi-color object detection and recognition in complex real-world scenarios.