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Dual Redox-active Covalent Organic Framework-based Memristors for Highly-efficient Neuromorphic Computing.

Qiongshan Zhang1, Qiang Che1, Dongchuang Wu1

  • 1Key Laboratory for Advanced Materials and Joint International Research, Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China.

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|August 6, 2024
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Summary

High-performance organic memristors using covalent organic frameworks (COFs) enable advanced neuromorphic computing. These memristors demonstrate 128 distinct states, significantly improving image recognition accuracy for complex tasks.

Keywords:
covalent organic frameworksdual redox-activeneuromorphic computingorganic memristorsingle-phase synthesis

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computer Engineering

Background:

  • Organic memristors are crucial for neuromorphic computing.
  • Covalent Organic Frameworks (COFs) offer promising properties for memristor applications.
  • High-quality COF synthesis is key to enhancing memristor performance.

Purpose of the Study:

  • To synthesize novel COF nanosheets for high-performance memristors.
  • To investigate the impact of dual redox-active centers on memristor characteristics.
  • To demonstrate the application of these memristors in neuromorphic computing tasks.

Main Methods:

  • A room-temperature single-phase method was used to synthesize Ta-Cu3 COF.
  • Acid-assisted exfoliation and spin-coating were employed to create COF films.
  • A convolutional neural network (CNN) was utilized for image recognition tasks.

Main Results:

  • The Ta-Cu3 COF memristor exhibited 128 non-volatile conductive states.
  • Dual redox-active centers and crystallinity reduced the redox energy barrier.
  • 95.13% image recognition accuracy was achieved for campus landmarks.
  • A 45.56% improvement in recognition accuracy was observed compared to binary state devices.

Conclusions:

  • The developed Ta-Cu3 COF memristor demonstrates superior performance for neuromorphic computing.
  • The multi-state conductivity of COF-based memristors enhances computational efficiency.
  • This work paves the way for advanced, high-accuracy neuromorphic systems.