Neuromorphic Nanoionics for Human–Machine Interaction : From Materials to Applications

Xuerong Liu, Cui Sun, Xiaoyu Ye, Xiaojian Zhu*, Cong Hu, Hongwei Tan*, Shang He, Mengjie Shao, Run Wei Li*

*Corresponding author for this work

Research output: Contribution to journalReview Articlepeer-review

1 Citation (Scopus)

Abstract

Human–machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Original languageEnglish
JournalAdvanced Materials
DOIs
Publication statusE-pub ahead of print - 29 Feb 2024
MoE publication typeA2 Review article, Literature review, Systematic review

Keywords

  • human–machine interaction
  • ion-gated transistor
  • nanoionic memristor
  • neuromorphic nanoionics
  • synapse and neuron

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