Protonic solid-state electrochemical synapse for physical neural networks
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, respecti...
| Main Authors: | , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2021
|
| Online Access: | https://hdl.handle.net/1721.1/129958 |
| _version_ | 1811091110868549632 |
|---|---|
| author | Yao, Xiahui Klyukin, Konstantin Lu, Wenjie Onen, Murat Ryu, Seungchan Kim, Dongha Emond, Nicolas del Alamo, Jesús A. Li, Ju Yildiz, Bilge |
| author2 | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Yao, Xiahui Klyukin, Konstantin Lu, Wenjie Onen, Murat Ryu, Seungchan Kim, Dongha Emond, Nicolas del Alamo, Jesús A. Li, Ju Yildiz, Bilge |
| author_sort | Yao, Xiahui |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:56:52Z |
| format | Article |
| id | mit-1721.1/129958 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:56:52Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1299582022-09-29T11:38:37Z Protonic solid-state electrochemical synapse for physical neural networks Yao, Xiahui Klyukin, Konstantin Lu, Wenjie Onen, Murat Ryu, Seungchan Kim, Dongha Emond, Nicolas del Alamo, Jesús A. Li, Ju Yildiz, Bilge 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 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. National Science Foundation (U.S.) (Award DMR - 1419807) United States. Department of Energy. Office of Science User Facility (Contract DE-SC0012704) Extreme Science and Engineering Discovery Environment (XSEDE) (Grant TG-DMR190038) 2021-02-22T19:41:07Z 2021-02-22T19:41:07Z 2020-06 2020-01 2020-12-07T19:45:43Z Article http://purl.org/eprint/type/JournalArticle 2041-1723 https://hdl.handle.net/1721.1/129958 Yao, Xiahui et al. “Protonic solid-state electrochemical synapse for physical neural networks.” Nature Communications, 11, 1 (June 2020): 3431 © 2020 The Author(s) en 10.1038/S41467-020-16866-6 Nature Communications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Yao, Xiahui Klyukin, Konstantin Lu, Wenjie Onen, Murat Ryu, Seungchan Kim, Dongha Emond, Nicolas del Alamo, Jesús A. Li, Ju Yildiz, Bilge Protonic solid-state electrochemical synapse for physical neural networks |
| title | Protonic solid-state electrochemical synapse for physical neural networks |
| title_full | Protonic solid-state electrochemical synapse for physical neural networks |
| title_fullStr | Protonic solid-state electrochemical synapse for physical neural networks |
| title_full_unstemmed | Protonic solid-state electrochemical synapse for physical neural networks |
| title_short | Protonic solid-state electrochemical synapse for physical neural networks |
| title_sort | protonic solid state electrochemical synapse for physical neural networks |
| url | https://hdl.handle.net/1721.1/129958 |
| work_keys_str_mv | AT yaoxiahui protonicsolidstateelectrochemicalsynapseforphysicalneuralnetworks AT klyukinkonstantin protonicsolidstateelectrochemicalsynapseforphysicalneuralnetworks AT luwenjie protonicsolidstateelectrochemicalsynapseforphysicalneuralnetworks AT onenmurat protonicsolidstateelectrochemicalsynapseforphysicalneuralnetworks AT ryuseungchan protonicsolidstateelectrochemicalsynapseforphysicalneuralnetworks AT kimdongha protonicsolidstateelectrochemicalsynapseforphysicalneuralnetworks AT emondnicolas protonicsolidstateelectrochemicalsynapseforphysicalneuralnetworks AT delalamojesusa protonicsolidstateelectrochemicalsynapseforphysicalneuralnetworks AT liju protonicsolidstateelectrochemicalsynapseforphysicalneuralnetworks AT yildizbilge protonicsolidstateelectrochemicalsynapseforphysicalneuralnetworks |