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

Journal: Advanced materials (Deerfield Beach, Fla.)
PMID: 38421081

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.

Authors

  • Xuerong Liu
    School of Psychology, Third Military Medical University, Chongqing, China.
  • Cui Sun
    CAS 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.
  • Xiaoyu Ye
    CAS 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.
  • Xiaojian Zhu
  • Cong Hu
    School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, China.
  • Hongwei Tan
    NanoSpin, Department of Applied Physics, Aalto University School of Science, P.O. Box 15100, FI-00076, Aalto, Finland. hongwei.tan@aalto.fi.
  • Shang He
    CAS 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.
  • Mengjie Shao
    College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, PR China.
  • Run-Wei Li
    CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China.

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