Resistive switching and magnetic modulation based on graphene oxide quantum dot-doped amorphous TiO<sub>2</sub> composites
Abstract The rapid expansion of data-intensive applications in the AI era necessitates innovative memory technologies that offer high-density storage and in-memory processing. In this study, we introduce graphene oxide quantum dots (GQDs) doped amorphous TiO 2 (a-TiO 2 ) as a dielectric layer in resistive random access memory (RRAM). This approach significantly enhances resistive and magnetic switching performance while enabling more stable four-state storage. The fabricated Ag/GQDs:a-TiO 2 /ITO device exhibits comparable endurance cycles (500 cycles) and retention time (12 000 s) to the undoped counterpart (Ag/a-TiO 2 /ITO), but with lower operating voltages (SET: 0.28 V, RESET: −0.25 V) and significantly improved operational stability(∼80% reduced variability). Notably, the magnetic variation ratio of the fabricated device increases to 5.7 (compared to 2.96 for Ag/a-TiO 2 /ITO), indicating that the presence of GQDs effectively enables electric field regulation of magnetism in RRAM. Material characterizations confirm the incorporation of GQDs into TiO 2 films, and the modulation of resistance and magnetism via electric field is primarily attributed to oxygen vacancies (V O s), which establish a resistive-magnetic synergistic coupling. This study provides a scalable approach for fabricating high-performance, multifunctional memory devices, thereby enabling the development of next-generation neuromorphic and spintronic systems.
Authors
- Fan Wang (ORCID: https://orcid.org/0000-0001-5788-0334)
- Ziwei Yue
- Zhenhua Li (ORCID: https://orcid.org/0009-0000-3276-744X)
- Yinan Jiao
- Jingjuan Wang (ORCID: https://orcid.org/0000-0002-5943-6605)
- Ling-Zhi Tang
- L. Liu (ORCID: https://orcid.org/0000-0002-2764-2885)
Institutions
- Xingtai University (CN)
- Shijiazhuang Tiedao University (CN)
- Hebei GEO University (CN)
Publication Details
- Journal
- Journal of Physics D Applied Physics
- Published
- 2025-09-09
- DOI
- https://doi.org/10.1088/1361-6463/ae04f2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Department of Education of Hebei Province
- Hebei GEO University