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Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing

Cited 22 time in Web of Science Cited 25 time in Scopus
Authors

Kang, Jaehyun; Kim, Taeyoon; Hu, Suman; Kim, Jaewook; Kwak, Joon Young; Park, Jongkil; Park, Jong Keuk; Kim, Inho; Lee, Suyoun; Kim, Sangbum; Jeong, YeonJoo

Issue Date
2022-07
Publisher
Nature Publishing Group
Citation
Nature Communications, Vol.13 No.1, p. 4040
Abstract
Memristors, or memristive devices, have attracted tremendous interest in neuromorphic hardware implementation. However, the high electric-field dependence in conventional filamentary memristors results in either digital-like conductance updates or gradual switching only in a limited dynamic range. Here, we address the switching parameter, the reduction probability of Ag cations in the switching medium, and ultimately demonstrate a cluster-type analogue memristor. Ti nanoclusters are embedded into densified amorphous Si for the following reasons: low standard reduction potential, thermodynamic miscibility with Si, and alloy formation with Ag. These Ti clusters effectively induce the electrochemical reduction activity of Ag cations and allow linear potentiation/depression in tandem with a large conductance range (similar to 244) and long data retention (similar to 99% at 1 hour). Moreover, according to the reduction potentials of incorporated metals (Pt, Ta, W, and Ti), the extent of linearity improvement is selectively tuneable. Image processing simulation proves that the Ti-4.8%:a-Si device can fully function with high accuracy as an ideal synaptic model.
ISSN
2041-1723
URI
https://hdl.handle.net/10371/184538
DOI
https://doi.org/10.1038/s41467-022-31804-4
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