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Ferroelectric P(VDF-TrFE) wrapped InGaAs nanowires for ultralow-power artificial synapses

  • Pengshan Xie
  • , Yulong Huang
  • , Wei Wang
  • , You Meng
  • , Zhengxun Lai
  • , Fei Wang
  • , Sen Po Yip
  • , Xiuming Bu
  • , Weijun Wang
  • , Dengji Li
  • , Jia Sun*
  • , Johnny C. Ho*
  • *Corresponding author for this work
  • City University of Hong Kong
  • Central South University
  • Kyushu University
  • Zhengzhou University

Research output: Contribution to journalArticlepeer-review

Abstract

The gallop of artificial intelligence ignites urgent demand on information processing systems with ultralow power consumption, reliable multi-parameter control and high operation efficiency. Here, the poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) wrapped InGaAs nanowire (NW) artificial synapses capable to operate with record-low subfemtojoule power consumption are presented. The essential synaptic behaviors are mimicked and modulated effectively by adjusting the thickness of top P(VDF-TrFE) films. Moreover, the long-term depression is realized by applying visible light (450 nm) because of the negative photoconductivity of InGaAs nanowires. Combined with optimal P(VDF-TrFE) films, the synaptic devices have the more linear long-term potentiation/depression characteristics and the faster supervised learning process simulated by hardware neural networks. The Pavlovian conditioning is also performed by combining electrical and infrared stimuli. Evidently, these ultralow-operating-power synapses are demonstrated with the brain-like behaviors, effective function modulation, and more importantly, the synergistic photoelectric modulation, which illustrates the promising potentials for neuromorphic computing systems.

Original languageEnglish
Article number106654
JournalNano Energy
Volume91
DOIs
StatePublished - Jan 2022
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Artificial synapse
  • Associative learning
  • Ferroelectric polymer
  • InGaAs nanowires
  • Negative photoconductivity

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