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Novel Optoelectronic Reconfigurable Transistors Based on Graphene/VO2 Heterojunction for Efficient Neuromorphic

Danke Chen1, Yuning Li1, Xiaoqiu Tang1

  • 1School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|September 26, 2025
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Summary

Researchers developed a novel optoelectronic reconfigurable neuromorphic transistor (ORNT) for artificial intelligence. This device integrates perception, computation, and storage, mimicking biological vision systems for advanced AI hardware.

Keywords:
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Area of Science:

  • Optoelectronics
  • Artificial Intelligence
  • Neuromorphic Engineering

Background:

  • Von Neumann architecture bottlenecks limit current computing.
  • Neuromorphic hardware is crucial for advancing artificial intelligence.
  • Biological vision systems offer inspiration for efficient information processing.

Purpose of the Study:

  • To design a novel optoelectronic reconfigurable neuromorphic transistor (ORNT).
  • To integrate optical information perception, computation, and storage functionalities.
  • To mimic the human visual nervous system in artificial devices.

Main Methods:

  • Fabrication of an electrode-inserted graphene/VO2 nanoparticles heterostructure.
  • Utilizing the photovoltaic effect for self-powered responsiveness.
  • Leveraging photogating and photoinduced phase transitions in VO2 for device function.

Main Results:

  • ORNT demonstrates broadband self-powered operation (365-940 nm).
  • Wide-electrode ORNTs exhibit synaptic behavior; narrow-electrode ORNTs show data storage and photomodulation.
  • An integrated optical communication and processing-in-memory system was successfully demonstrated.

Conclusions:

  • ORNTs offer an innovative strategy for neuromorphic hardware.
  • The device optimizes chip hardware resource allocation.
  • Enhanced adaptability and scalability of AI systems are achieved.