Nano-Memristors with 4 mV Switching Voltage Based on Surface-Modified Copper Nanoparticles

Peisong Liu, Fei Hui, Fernando Aguirre, Fernan Saiz, Lulu Tian, Tingting Han, Zhijun Zhang, Enrique Miranda, Mario Lanza*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The development of memristors operating at low switching voltages <50 mV can be very useful to avoid signal amplification in many types of circuits, such as those used in bioelectronic applications to interact with neurons and nerves. Here, it is reported that 400 nm-thick films made of dalkyl-dithiophosphoric (DDP) modified copper nanoparticles (CuNPs) exhibit volatile threshold-type resistive switching (RS) at ultralow switching voltage of ≈4 mV. The RS is observed in small nanocells with a lateral size of <50 nm–2, during hundreds of cycles, and with an ultralow variability. Atomistic calculations reveal that the switching mechanism is related to the modification of the Schottky barriers and insulator-to-metal transition when ionic movement is induced via external bias. The devices are also used to model integrate-and-fire neurons for spiking neural networks and it is concluded that circuits employing DDP-CuNPs consume around ten times less power than similar neurons implemented with a memristor that switches at 40 mV.

Original languageEnglish (US)
Article number2201197
JournalAdvanced Materials
Volume34
Issue number20
DOIs
StatePublished - May 19 2022

Bibliographical note

Funding Information:
P.L. and F.H. contributed equally to this work. This work was supported by the Ministry of Science and Technology of China (grants no. 2019YFE0124200 and 2018YFE0100800), and the National Natural Science Foundation of China (grants no. 61874075), the Science and Technology Planning Project of Henan Province (212102210466), the first‐class subject cultivation project of Henan University (2019YLZDJL12), as well as by the generous Baseline funding program of the King Abdullah University of Science and Technology.

Funding Information:
P.L. and F.H. contributed equally to this work. This work was supported by the Ministry of Science and Technology of China (grants no. 2019YFE0124200 and 2018YFE0100800), and the National Natural Science Foundation of China (grants no. 61874075), the Science and Technology Planning Project of Henan Province (212102210466), the first-class subject cultivation project of Henan University (2019YLZDJL12), as well as by the generous Baseline funding program of the King Abdullah University of Science and Technology.

Publisher Copyright:
© 2022 Wiley-VCH GmbH.

Keywords

  • copper nanoparticles
  • density functional theory
  • memristors
  • resistive switching
  • spiking neural networks

ASJC Scopus subject areas

  • General Materials Science
  • Mechanics of Materials
  • Mechanical Engineering

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