journal article·2025-05-01Open access

Exploiting galvanic effect in Ag–Al alloy ion reservoir of resistive switching device for high-density cross-point array memory system

Seokjae Lim, Jiyong Woo

AIP Advances· Volume 15· Issue 5

Abstract

A silver (Ag)-based two-terminal semiconductor device exhibits resistive switching behavior driven by Ag+ ion migration. The migration of Ag+ ions, generated through the oxidation of the Ag electrode, forms a Ag filament, resulting in a low-resistance state. Conversely, the outward diffusion of Ag+ ions, caused by the oxidation of neutral Ag within the filament, leads to the dissolution of the filament and a high-resistance state. Typically, in Ag-based two-terminal devices, the filament dissolves when the externally applied voltage is reduced, resulting in volatile threshold-switching characteristics. In this study, we observed that the introduction of an aluminum (Al) layer above or below the Ag electrode increases the electrical energy required for the filament dissolution. The presence of the Al element, which has a lower standard reduction potential than Ag, induces the galvanic effect, which suppresses the generation of Ag+ ions from the Ag filament. This, in turn, raises the electrical energy needed for filament dissolution, resulting in non-volatile memory-switching characteristics. To stabilize the memory-switching characteristics, the optimal amount of Al was explored by creating an Ag–Al alloy ion reservoir via combinatorial sputtering. Based on experimental findings, a resistive switching device with an Ag0.5Al0.5 alloy ion reservoir effectively supplied the optimal amount of Ag+ ions to the HfO2 electrolyte, enhancing the stability of the Ag filament and enabling non-volatile memory-switching. The one-selector–one-resistor (1S–1R) configuration was successfully achieved by depositing pure Ag and Ag0.5Al0.5 ionic reservoirs side-by-side on top of the HfO2 electrolyte, resulting in a monolithically integrated 1S–1R structure.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Transition Metal Oxide Nanomaterials · Neuroscience and Neural Engineering

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

IC Design Education Center, Kyungpook National University 1 , Daegu 41566,

Advanced Energy Research Center of Digital Convergence Division, Gumi Electronics and Information Technology Research Institute 2 , Gumi 39171,

School of Electronic and Electrical Engineering, Kyungpook National University 3 , Daegu 41566,

Funding

Ministry of Trade, Industry and Energy · Institute for Information and Communications Technology Promotion

References (25)

Show source-provided references
1. 2017 · 1
2. 2013 · 13
3. Adv. Mater. · 2009 · 21 · 263210.1002/adma.200900375 ↗
4. J. Vac. Sci. Technol. B · 2014 · 32 · 04080210.1116/1.4889999 ↗
5. 2013
6. 2012
7. 2014 · 190
8. 2010 · 205
9. IEEE Electron Device Lett. · 2015 · 36 · 68110.1109/led.2015.2430332 ↗
10. Nat. Mater. · 2010 · 9 · 40310.1038/nmat2748 ↗
11. Appl. Phys. Lett. · 2017 · 111 · 06310910.1063/1.4985165 ↗
12. 2009 · 130
13. 2012
14. J. Phys. Chem. Ref. Data · 1989 · 18 · 110.1063/1.555839 ↗
15. Adv. Funct. Mater. · 2018 · 28 · 170486210.1002/adfm.201704862 ↗
16. ECS Solid State Lett. · 2014 · 3 · 12010.1149/2.0031410ssl ↗
17. J. Appl. Phys. · 1961 · 32 · 36510.1063/1.1736012 ↗
18. Surf. Coat. Technol. · 2011 · 205 · S110.1016/j.surfcoat.2011.03.081 ↗
19. 2013
20. 2013
21. Adv. Mater. · 2018 · 30 · 170519310.1002/adma.201705193 ↗
22. 2017
23. Adv. Mater. · 2017 · 29 · 160445710.1002/adma.201604457 ↗
24. Nat. Mater. · 2016 · 16 · 10110.1038/nmat4756 ↗
25. 2016