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Updated: Jul 7, 2026

08:07
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Reconfigurable Photoelectric Coaxial Fiber-Based Memristors for Neuromorphic Computing
Keying Li1, Jieru Song1, Chen Lu1
1State Key Laboratory of Integrated Chips and Systems, College of Integrated Circuits & Micro-Nano Electronics, School of Microelectronics, Fudan University, Shanghai 200433, China.
ACS Nano
|July 6, 2026
Summary
Researchers developed flexible, fiber-based neuromorphic devices that mimic brain functions. These bioinspired optoelectronic devices show promise for wearable applications, achieving high accuracy in image and speech recognition tasks.
Area of Science:
- Materials Science
- Neuroscience
- Electrical Engineering
Background:
- Traditional neuromorphic devices face limitations in flexibility and wearability.
- Bioinspired optoelectronic devices offer an alternative to von Neumann architectures.
Purpose of the Study:
- To demonstrate a flexible, fiber-based memristor for neuromorphic applications.
- To enable devices to function as both synapses and neurons.
Main Methods:
- Fabrication of a Pt/TiOx/NiOx/Au memristor on coaxial fibers using electron beam evaporation.
- Testing device reconfigurability between synaptic and neuronal functions under electrical and optical stimuli.
- Implementing reservoir computing for image and speech recognition tasks.
Main Results:
- The device successfully emulated neuronal integrate-and-fire behavior electrically and nonvolatile synaptic functions optically.
- Achieved high recognition rates (93.0% for fashion images, 98.6% for spoken digits) using reservoir computing.
- Demonstrated robust stability and flexibility under mechanical deformations (bending).
Conclusions:
- The developed fiber-based memristor is suitable for highly flexible and wearable neuromorphic applications.
- The device's reconfigurable nature and stable performance highlight its potential for advanced computing systems.
- This work paves the way for next-generation fiber-based neuromorphic engineering.
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