AlN-based unipolar memristors with symmetrical Al electrodes: Preparation, characterization, and test
Ultra-wide-bandgap semiconductor aluminum nitride (AlN) has demonstrated many excellent characteristics and is a promising candidate for advanced memristor dielectrics. Numerous stacked structures have been attempted in recent years, but research on simpler symmetrical electrodes structure and unipolarity is still insufficient. Here, we designed symmetrical Al electrodes structure with unipolarity that can realize Forming, Set, and Reset processes through voltage in only one direction. With good consistency, we can achieve ROFF/RON ratio of >105, resistance states of >8, retention time of >104 s, and pulse programmable characteristics. Through the research on temperature and size, we have revealed the asymmetric conduction mechanism under the symmetrical structure. At the same time, we also achieved the integration of erasable and non-erasable arrays on a single chip. Compatibility with CMOS technology indicates that our devices will be very promising for integration with other modules, providing inspiration for enriching the application of AlN-based memristors.
Authors
- Haiming Qin (ORCID: https://orcid.org/0009-0003-2140-1620)
- Nan He (ORCID: https://orcid.org/0000-0002-8885-2907)
- Xinpeng Wang (ORCID: https://orcid.org/0009-0005-0041-0135)
- Yi Tong (ORCID: https://orcid.org/0000-0001-8146-3549)
- Weijing Shao (ORCID: https://orcid.org/0009-0006-7342-4121)
- Yi Liu (ORCID: https://orcid.org/0000-0002-5333-7163)
- Hanbing Fang (ORCID: https://orcid.org/0000-0001-5112-3658)
- Yu Wang (ORCID: https://orcid.org/0000-0002-3883-5578)
- Ruifa Hu (ORCID: https://orcid.org/0009-0008-4559-315X)
- Cong Han
- Hao Zhang (ORCID: https://orcid.org/0009-0001-9987-5186)
- Jiawen Wu (ORCID: https://orcid.org/0000-0002-9160-7304)
Institutions
- Soochow University (CN)
- Nanjing University of Posts and Telecommunications (CN)
- Suzhou Research Institute (CN)
- State Key Laboratory of Millimeter Waves
- Southeast University (CN)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1063/5.0279814
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73