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2D Material-Based Bioinspired Devices for Neuromorphic Computing.

Chenguang Zhu1, Guangcheng Wu1, Xingxia Sun1

  • 1Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, 410082, China.

Small (Weinheim an Der Bergstrasse, Germany)
|August 16, 2025
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Summary
This summary is machine-generated.

Two-dimensional (2D) materials offer efficient neuromorphic computing solutions, overcoming traditional architecture limits. These materials enable advanced artificial intelligence applications by mimicking brain functions for faster, low-power processing.

Keywords:
2D materialsneuromorphic computingneuromorphic devices

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Neuroscience

Background:

  • Traditional von Neumann architecture faces memory and power bottlenecks, limiting AI advancement.
  • Neuromorphic computing, inspired by the brain, promises high energy efficiency and speed for post-Moore era computing.
  • Two-dimensional (2D) materials possess unique properties making them ideal for next-generation neuromorphic devices.

Purpose of the Study:

  • To review the progress of 2D materials in neuromorphic computing.
  • To highlight advancements in device design, performance, and applications.
  • To identify challenges and future opportunities in the field.

Main Methods:

  • Systematic review of recent literature on 2D neuromorphic devices.
  • Analysis of various device architectures (memristors, FETs, optoelectronic transistors).
  • Exploration of material synthesis, device uniformity, and integration strategies.

Main Results:

  • 2D materials successfully emulate synaptic plasticity and neuronal dynamics.
  • Demonstrated potential for multi-modal sensory integration in edge computing and autonomous systems.
  • Progress in structural design and performance optimization of 2D neuromorphic devices.

Conclusions:

  • 2D materials are crucial for overcoming current computing bottlenecks.
  • Further research is needed in wafer-scale synthesis, device uniformity, and large-scale integration.
  • Future directions include advanced electronics and heterogeneous integration architectures for 2D neuromorphic computing.