Deterministic selector–memristor mode switching in SnSe-based devices via ultraviolet-mediated interface engineering
Resistive switching devices are promising candidates for high-density memory and in-memory computing applications, but sneak-path leakage in crossbar arrays requires selector elements with strong nonlinearity. Here, we demonstrate deterministic switching-mode selection in solution-processed SnSe-based Al/AlOx/SnSe/Ag devices via ultraviolet-mediated interface engineering (UIE). By varying only the UIE time (tUIE) of the Al bottom electrode, the interfacial oxidation state was elaborately manipulated, as manifested by contact angle measurements and comprehensive spectro-microscopic surveys entailing x-ray photoelectron spectroscopy and cross-sectional scanning transmission electron microscopy observations. The pristine device (tUIE = 0 min) exhibited stable memristor operation with nonvolatile resistive switching, whereas the moderately oxidized device (tUIE = 10 min) showed selector operation with volatile threshold switching and a nonlinearity of approximately 2.25 × 103. In contrast, the excessively oxidized devices (tUIE = 20 min) revealed breakdown-like irreversible high-conductance behavior, which signals the existence of an optimized interfacial oxidation window for switching-mode control. These results propose newly devised UIE as a straightforward and indisputable strategy for deterministic selector–memristor mode control within a common SnSe-based device platform.
Authors
- Sungjune Jung (ORCID: https://orcid.org/0000-0001-9258-0572)
- Sunhyuk Lim (ORCID: https://orcid.org/0009-0001-5079-5812)
- Wooseok Song (ORCID: https://orcid.org/0000-0002-0487-2055)
- Hyunjin Park (ORCID: https://orcid.org/0000-0003-1838-8149)
- Dohyung Lee
Institutions
- Pohang University of Science and Technology (KR)
- Korea Research Institute of Chemical Technology (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2026-10-05
- DOI
- https://doi.org/10.1063/5.0353240
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00