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Related Experiment Video

Updated: Jan 18, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Optically Controlled Memristor Enabling Synergistic Sensing-Memory-Computing for Neuromorphic Vision Systems.

Jianhui Zhao1, Dingxin Liu1, Kangbo Zhao1

  • 1Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China.

Advanced Materials (Deerfield Beach, Fla.)
|September 10, 2025
PubMed
Summary

This study introduces a new light-controlled memristor for neuromorphic vision systems. The device demonstrates high performance in sensing, memory, and computing tasks, overcoming limitations of current technologies.

Keywords:
neuromorphic visual system (NVS)optically controlled memristor (OCM)sensing‐memory‐computing (SMC)

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Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • Neuromorphic visual devices offer potential for integrated sensing, memory, and computing in artificial vision systems.
  • Current devices struggle with light/dark resistance ratios, transient photo-response, and data retention, hindering complex system integration.

Purpose of the Study:

  • To develop a novel optically controlled memristor for enhanced neuromorphic vision systems.
  • To address limitations in current neuromorphic device performance, particularly in complex lighting conditions.

Main Methods:

  • Developed a KNbO3-based optically controlled memristor.
  • Investigated resistive switching triggered by light-modulated oxygen vacancy charges.
  • Evaluated device performance across the 405-650 nm visible spectrum.

Main Results:

  • Achieved optically triggered nonvolatile resistive switching with a switching ratio >10^2.
  • Demonstrated high endurance (6 × 10^6 cycles) and retention (>10^4 s).
  • Exhibited a photo-to-dark current ratio >10^2 and self-powered capability.

Conclusions:

  • The novel memristor significantly improves visual perception in complex lighting environments.
  • The device integrates transient signal processing with persistent information retention for bioinspired vision chips.
  • Achieved high recognition accuracies (96% and 85%) on benchmark datasets, demonstrating practical feasibility.