Skip to main navigation Skip to search Skip to main content

Ultralow Energy Consumption Angstrom-Fluidic Memristor

  • Deli Shi
  • , Wenhui Wang
  • , Yizheng Liang
  • , Libing Duan
  • , Guanghua Du
  • , Yanbo Xie
  • Northwestern Polytechnical University Xian
  • CAS - Institute of Modern Physics

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

The emergence of nanofluidic memristors has made a giant leap to mimic the neuromorphic functions of biological neurons. Here, we report neuromorphic signaling using Angstrom-scale funnel-shaped channels with poly-l-lysine (PLL) assembled at nano-openings. We found frequency-dependent current-voltage characteristics under sweeping voltage, which represents a diode in low frequencies, but it showed pinched current hysteresis as frequency increases. The current hysteresis is strongly dependent on pH values but weakly dependent on salt concentration. We attributed the current hysteresis to the entropy barrier of PLL molecules entering and exiting the Angstrom channels, resulting in reversible voltage-gated open-close state transitions. We successfully emulated the synaptic adaptation of Hebbian learning using voltage spikes and obtained a minimum energy consumption of 2-23 fJ in each spike per channel. Our findings pave a new way to mimic neuronal functions by Angstrom channels in low energy consumption.

Original languageEnglish
Pages (from-to)11662-11668
Number of pages7
JournalNano Letters
Volume23
Issue number24
DOIs
StatePublished - 27 Dec 2023

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

  • latent track channel
  • memristor
  • nanofluidics
  • neuromorphic computing

Fingerprint

Dive into the research topics of 'Ultralow Energy Consumption Angstrom-Fluidic Memristor'. Together they form a unique fingerprint.

Cite this