Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence.

Journal: Small (Weinheim an der Bergstrasse, Germany)
PMID: 38149504

Abstract

Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.

Authors

  • Chuan Yang
    Department of Clinical Genetics, Shengjing Hospital of China Medical University, Shenyang 110004, China; Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang 110004, China.
  • Hongyan Wang
    State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200432, China.
  • Zelin Cao
    Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.
  • Xiaoliang Chen
    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi 710049, P.R. China.
  • Guangdong Zhou
    School of Artificial Intelligence, Southwest University, Chongqing 400715, China.
  • Hongbin Zhao
    State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co., Ltd., Beijing 100088, China.
  • Zhenhua Wu
    School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
  • Yong Zhao
    a School of Mathematics and Information Science , Henan Polytechnic University , Jiaozuo 454000 , People's Republic of China.
  • Bai Sun
    Department of Mechanical and Mechatronics Engineering, Waterloo Institute for Nanotechnology, Centre for Advanced Materials Joining, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.

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