Protonic solid-state electrochemical synapse for physical neural networks
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s41467-020-16866-6.pdf
Description
Published version
Size
1.86 MB
Format
Adobe PDF
Checksum (MD5)
1650224a5def90374c5075b542bc3d12
Author(s)
Yao, Xiahui
Klyukin, Konstantin
Lu, Wenjie
Onen, Murat
Ryu, Seungchan
Kim, Dongha
Emond, Nicolas
del Alamo, Jesús A.
Li, Ju
Yildiz, Bilge
Date Issued
June 2020
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
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.
MIT 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
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41467-020-16866-6
