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

Jaehyun Kang, Taeyoon Kim, Suman Hu, Jaewook Kim, Joon Young Kwak, Jongkil Park, Jong Keuk Park, Inho Kim, Suyoun Lee, Sangbum Kim and YeonJoo Jeong ()
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Jaehyun Kang: Korea Institute of Science and Technology
Taeyoon Kim: Korea Institute of Science and Technology
Suman Hu: Korea Institute of Science and Technology
Jaewook Kim: Korea Institute of Science and Technology
Joon Young Kwak: Korea Institute of Science and Technology
Jongkil Park: Korea Institute of Science and Technology
Jong Keuk Park: Korea Institute of Science and Technology
Inho Kim: Korea Institute of Science and Technology
Suyoun Lee: Korea Institute of Science and Technology
Sangbum Kim: Seoul National University
YeonJoo Jeong: Korea Institute of Science and Technology

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: 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 (~244) and long data retention (~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 Ti4.8%:a-Si device can fully function with high accuracy as an ideal synaptic model.

Date: 2022
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DOI: 10.1038/s41467-022-31804-4

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