2D Piezo-Ferro-Opto-Electronic Artificial Synapse for Bio-Inspired Multimodal Sensory Integration.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date: Apr 7, 2025

Abstract

Multimodal sensory integration is vital for the evolution of artificial intelligence, yet current approaches often rely on physically connecting distinct sensing units (such as visual and tactile devices) through external circuits, leading to data transmission delays and information loss. Here, a groundbreaking paradigm is demonstrated for integrating visual-tactile fusion perception in one device with a single functional material. This is achieved by developing an unprecedented 2D Piezo-Ferro-Opto-Electronic (PFOE) Artificial Synapse, which combines the comprehensive ferroelectricity (for synaptic behaviors), piezoelectricity (for tactile modulation), and optoelectronic responsiveness (for visual detection) of strained 2D NbOI. Under the synergistic influence of light and strain, the device exhibits remarkable persistent photoconductivity (PPC), a notable increase in paired-pulse facilitation (PPF) index (from 116% to 180%), and a reduction in the power exponent of the sublinear power-law fitting photocurrent curve (from 0.797 to 0.376). These features enhance the clarity and recognition of fingerprint images that integrate visual and tactile information. The work provides a robust foundation for integrating multisensory capabilities into advanced human-machine interfaces and artificial intelligence systems, marking a significant leap forward in the development of multifunctional neuromorphic devices.

Authors

  • Mengqi Wang
    Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Qilu University of Technology (Shandong Academy of Science), Jinan 250353, China.
  • Decai Ouyang
    State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, P. R. China.
  • Yin Dai
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; Engineering Center on Medical Imaging and Intelligent Analysis, Ministry Education, Northeastern University, Shenyang 110169, China. Electronic address: daiyin@bmie.neu.edu.cn.
  • Da Huo
    School of Management, Xi'an Jiaotong University, Xi'an, 710049, China.
  • Wenke He
    State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, P. R. China.
  • Bailing Song
    School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, Hubei, 430070, P. R. China.
  • Wenhua Hu
    School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China.
  • Menghao Wu
    School of Physics and Institute for Quantum Science and Engineering, School of Chemistry and Institute of Theoretical Chemistry, Huazhong University of Science and Technology (HUST), Wuhan, 430074, P. R. China.
  • Yuan Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Tianyou Zhai
    State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, P. R. China.

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