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Protonic solid-state electrochemical synapse for physical neural networks

Author(s)
Yao, Xiahui; Klyukin, Konstantin; Lu, Wenjie; Onen, Murat; Ryu, Seungchan; Kim, Dongha; Emond, Nicolas; del Alamo, Jesús A.; Li, Ju; Yildiz, Bilge; ... Show more Show less
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Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/
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Abstract
Physical neural networks made of analog resistive switching processors are promising platforms for analog computing. State-of-the-art resistive switches rely on either conductive filament formation or phase change. These processes suffer from poor reproducibility or high energy consumption, respectively. Herein, we demonstrate the behavior of an alternative synapse design that relies on a deterministic charge-controlled mechanism, modulated electrochemically in solid-state. The device operates by shuffling the smallest cation, the proton, in a three-terminal configuration. It has a channel of active material, WO3. A solid proton reservoir layer, PdHx, also serves as the gate terminal. A proton conducting solid electrolyte separates the channel and the reservoir. By protonation/deprotonation, we modulate the electronic conductivity of the channel over seven orders of magnitude, obtaining a continuum of resistance states. Proton intercalation increases the electronic conductivity of WO3 by increasing both the carrier density and mobility. This switching mechanism offers low energy dissipation, good reversibility, and high symmetry in programming.
Date issued
2020-06
URI
https://hdl.handle.net/1721.1/129958
Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering; Massachusetts Institute of Technology. Department of Materials Science and Engineering; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Mechanical Engineering; Massachusetts Institute of Technology. Microsystems Technology Laboratories
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Yao, Xiahui et al. “Protonic solid-state electrochemical synapse for physical neural networks.” Nature Communications, 11, 1 (June 2020): 3431 © 2020 The Author(s)
Version: Final published version
ISSN
2041-1723

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