Multi-factors-regulated memristor based on Sm-doped Pb(Mg1/3Nb2/3)O3–PbTiO3 for artificial neural network

  • Fulai Lin
  • , Zhuoqun Li
  • , Bai Sun
  • , Wei Peng
  • , Zelin Cao
  • , Kaikai Gao
  • , Yu Cui
  • , Kun Zhu
  • , Qiang Lu
  • , Jinglei Li
  • , Yi Lyu
  • , Fenggang Ren

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The memristor, characterized by its resistive switching (RS) behavior, has garnered significant interest within the scientific community, particularly because of its vast potential applications in the fields of artificial intelligence (AI) and information storage. This is attributed to its unique properties, which align well with the requirements of advanced computational and memory systems. Ferroelectric memristors are currently a thriving area of research, and this study uses Sm-doped Pb(Mg1/3Nb2/3)O3–PbTiO3 (Sm-PMN-PT) and polyvinylidene difluoride (PVDF) as the functional layer. A multi-factor responsive memristor based on a Ag/Sm-PMN-PT:PVDF/ITO sandwich structure is fabricated, for which the RS behavior of the memristor can be adjusted by multi-factors such as voltage scanning rate, bias voltage amplitude, temperature and environmental humidity. Specifically, this device is sensitive to changes in environmental humidity and exhibits the properties of an artificial neural synapse. These advantageous characteristics endow this device with great potential for use in environmental sensors and artificial neural network (ANN) systems.

Original languageEnglish
Article number100506
JournalMaterials Today Advances
Volume22
DOIs
StatePublished - Jun 2024

Keywords

  • Artificial neural network
  • Ferroelectric material
  • Memristor
  • Multi-factors regulated
  • Multifunctional device

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