Protonic solid-state electrochemical synapse for physical neural networks

Xiahui Yao, Konstantin Klyukin, Wenjie Lu, Murat Onen, Seungchan Ryu, Dongha Kim, Nicolas Emond, Iradwikanari Waluyo, Adrian Hunt, Jesús A. del Alamo (), Ju Li () and Bilge Yildiz ()
Additional contact information
Xiahui Yao: Massachusetts Institute of Technology
Konstantin Klyukin: Massachusetts Institute of Technology
Wenjie Lu: Massachusetts Institute of Technology
Murat Onen: Massachusetts Institute of Technology
Seungchan Ryu: Massachusetts Institute of Technology
Dongha Kim: Massachusetts Institute of Technology
Nicolas Emond: Massachusetts Institute of Technology
Iradwikanari Waluyo: Brookhaven National Laboratory
Adrian Hunt: Brookhaven National Laboratory
Jesús A. del Alamo: Massachusetts Institute of Technology
Ju Li: Massachusetts Institute of Technology
Bilge Yildiz: Massachusetts Institute of Technology

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: 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: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-020-16866-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16866-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-16866-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().