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Related Experiment Video

Updated: Jan 18, 2026

Atomic Layer Deposition of Vanadium Dioxide and a Temperature-dependent Optical Model

Atomic Layer Deposition of Vanadium Dioxide and a Temperature-dependent Optical Model

Published on: May 23, 2018

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

Yuanduo Qu¹, Mengdi Hao^1,2, Haoran Hao³

¹Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, P. R. China.

Advanced Materials (Deerfield Beach, Fla.)

|September 12, 2025

Summary

This summary is machine-generated.

A novel vanadium carbide/oxide heterostructure memristor enables efficient near-infrared (NIR) detection and object recognition. This breakthrough offers a low-power, high-accuracy platform for autonomous systems and surveillance applications.

Keywords:

2D heterostructure near infrared object recognition topochemical conversion volatile memristor

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Area of Science:

Materials Science
Nanotechnology
Neuromorphic Engineering

Background:

Near-infrared (NIR) photon detection and object recognition are vital for autonomous navigation and surveillance.
Conventional systems face energy inefficiency and data bottlenecks.
Advanced materials are needed for next-generation neuromorphic devices.

Purpose of the Study:

To design and synthesize a novel heterostructure for enhanced NIR detection and recognition.
To develop a memristor-based artificial neural network (ANN) for complex object identification.
To overcome limitations of traditional photodetector and computing systems.

Main Methods:

A vanadium carbide/oxide (V₂C/V₂O_5-x) heterostructure was synthesized via topochemical conversion.
The heterostructure's memristor exhibited stable threshold-type resistance switching (RS) behavior.
NIR responsivity and multi-color modulable RS characteristics were investigated.

Main Results:

The V₂C/V₂O_5-x memristor showed stable RS over thousands of cycles and long-term storage.
Devices demonstrated linear threshold voltage variation with NIR light power and wavelength.
An ANN achieved high recognition accuracies (89.6% cars, 85.9% persons) using YOLOv7.

Conclusions:

The V₂C/V₂O_5-x heterostructure offers versatile functionalities for neuromorphic devices.
A memristor-based ANN platform enables robust multi-color object detection in real-world scenarios.
This work advances NIR detection and recognition for autonomous and surveillance technologies.