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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Reconfigurable Neuromorphic Computing Using Methyl-Engineered One-Dimensional Covalent Organic Framework Memristors.

Pan-Ke Zhou¹, Ziyue Yu¹, Tao Zeng²

¹State Key Laboratory of Photocatalysis on Energy and Environment, and Key Laboratory of Advanced Carbon-Based Functional Materials, College of Chemistry, Fuzhou University, Fujian 350116, China.

|March 26, 2025

Summary

This summary is machine-generated.

Methyl-engineered covalent organic frameworks (COFs) create stable memristors for neuromorphic computing. These devices offer energy-efficient, high-accuracy AI by mimicking brain functions with enhanced stability and multilevel storage.

Keywords:

Localized polarization effects Memristors Multilevel memory device Neuromorphic computing One-dimensional covalent organic frameworks

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Area of Science:

Materials Science
Neuroscience
Computer Science

Background:

Neuromorphic devices aim to emulate biological neural networks for efficient AI.
Current systems face challenges in scalability and energy efficiency.

Purpose of the Study:

To develop a transformative platform for reconfigurable neuromorphic computing.
To engineer methyl-functionalized one-dimensional covalent organic frameworks (1D COFs) for memristor applications.

Main Methods:

Incorporation of methyl groups into 1D COF structures.
Fabrication of memristors utilizing these engineered COFs.
Characterization of device performance in multilevel storage and synaptic emulation.

Main Results:

Methyl groups mitigated Ag+ migration, stabilizing conductive filaments.
Achieved devices with exceptional multilevel storage, stability, linearity, and reproducibility.
Demonstrated precise control over resistive switching for synaptic functions and ANNs.

Conclusions:

Methyl-engineered 1D COFs provide a promising foundation for next-generation neuromorphic computing.
These memristors exhibit high accuracy in tasks like image recognition.
The platform enables energy-efficient and scalable AI solutions.