4.8 Article

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

Journal

ADVANCED MATERIALS
Volume 34, Issue 20, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202201197

Keywords

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

Funding

  1. Ministry of Science and Technology of China [2019YFE0124200, 2018YFE0100800]
  2. National Natural Science Foundation of China [61874075]
  3. Science and Technology Planning Project of Henan Province [212102210466]
  4. Henan University [2019YLZDJL12]
  5. King Abdullah University of Science and Technology

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The development of memristors operating at low switching voltages below 50 mV is important for avoiding signal amplification in various circuits. A 400 nm-thick film made of DDP-modified copper nanoparticles exhibits volatile threshold-type resistive switching at approximately 4 mV. The devices are also used to model integrate-and-fire neurons, demonstrating that circuits employing DDP-CuNPs consume around ten times less power than those with memristors switching at 40 mV.
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 approximate to 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.

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