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Designing carbon conductive filament memristor devices for memory and electronic synapse applications

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Abstract

Electronic synaptic memristor systems have the potential to bring revolutionary change to traditional computer structures and to lay a solid foundation for the development of computer architectures simulating artificial brains. Among them, silver (Ag) or copper (Cu) filament-based memristor devices have increasingly attracted attention due to their excellent functional properties in plasticity and as memristors. However, the randomly dynamic process of nucleation during device fabrication results in nonuniform switching parameters. Here, we demonstrate the viability of a high-performance neuromorphic memristor device based on a carbon conductive filament mechanism, with the advantages of high switching stability and low power consumption. The memristor is also able to emulate faithfully different functions of artificial synapses, including paired-pulse facilitation (PPF) and spike-timing-dependent plasticity (STDP). According to detailed electron energy loss spectroscopy (EELS) and transmission electron microscopy (TEM) characterization, it is confirmed that carbon conductive filaments are formed in aluminum nitride (AlN) films comprising the middle layer of the memristor. First principles calculations provide insight into the energetics of defects involved in the diffusion of carbon atoms into the AlN film. This work probes the viability of a new physical conduction mechanism for use in neuromorphic memristor performance, with evidence of improved device performance.

Graphical abstract: Designing carbon conductive filament memristor devices for memory and electronic synapse applications

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Article information


Submitted
22 Oct 2019
Accepted
28 Nov 2019
First published
29 Nov 2019

Mater. Horiz., 2020, Advance Article
Article type
Communication

Designing carbon conductive filament memristor devices for memory and electronic synapse applications

Z. Zhou, J. Zhao, A. P. Chen, Y. Pei, Z. Xiao, G. Wang, J. Chen, G. Fu and X. Yan, Mater. Horiz., 2020, Advance Article , DOI: 10.1039/C9MH01684H

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