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Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications.

Xuerong Liu1,2,3, Cui Sun1,2, Xiaoyu Ye1,2

  • 1CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.

Advanced Materials (Deerfield Beach, Fla.)
|February 29, 2024
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Summary
This summary is machine-generated.

Neuromorphic computing using nanoionic devices enhances human-machine interaction (HMI) by mimicking the brain for efficient processing. This review explores nanoionic technologies for next-generation HMI, focusing on their applications and potential.

Keywords:
human–machine interactionion‐gated transistornanoionic memristorneuromorphic nanoionicssynapse and neuron

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Area of Science:

  • Neuroscience and Computer Engineering
  • Materials Science and Nanotechnology

Background:

  • Human-machine interaction (HMI) demands advanced intelligent technologies for seamless human-machine communication.
  • Emerging HMI domains include healthcare, machine perception, and biointerfaces.
  • Neuromorphic computing, inspired by the brain's architecture, offers efficient information processing.

Purpose of the Study:

  • To review recent developments in nanoionic device-based neuromorphic computing.
  • To explore the role of these technologies in next-generation HMI.
  • To evaluate performance metrics and discuss future opportunities.

Main Methods:

  • Examining fundamental mechanisms of nanoionic memristors and ion-gated transistors.
  • Evaluating key performance metrics: reliability, energy efficiency, flexibility, and biocompatibility.
  • Analyzing potential applications, challenges, and opportunities in HMI.

Main Results:

  • Nanoionic devices can emulate neuronal and synaptic functions.
  • Performance metrics indicate significant potential for neuromorphic computing in HMI.
  • The review highlights the fusion of humans and machines through advanced HMI technologies.

Conclusions:

  • Nanoionic neuromorphic computing is pivotal for advancing HMI.
  • Further research and development are crucial for overcoming challenges and realizing full potential.
  • These technologies promise to revolutionize human-machine integration.