Engineering oxygen vacancies via Ar+ ion implantation in single-layer HfO2 self-rectifying memristors
Metal-oxide self-rectifying memristors (SRMs) are promising candidates for selector-free high-density three-dimensional (3D) computing-in-memory (CIM) systems, yet the role of oxygen vacancy (OV) states in governing the self-rectifying memristive characteristics remains poorly understood. Here, argon ion (Ar+) implantation is employed as a post-deposition defect engineering strategy to regulate OV defect states in Ta/HfO2/Pt SRMs. The effect of the OV concentration and distribution on self-rectifying memristive performance is clarified. The optimized implantation condition (1 × 1015 ions/cm2, 15 keV) yields a rectification ratio (RR), nonlinearity (NL), and on/off ratio exceeding 105, as well as excellent switching uniformity. The device can be repeatedly programmed for more than 104 cycles and has robust retention for more than 104 s and a calculated scalability of 32 Gbit. Comparative experiments confirm that the Ar+ ion implantation engineered OV profile, featuring a higher concentration and an ordered OV distribution near the Ta electrode and a lower OV concentration near the Pt electrode, can balance trap-assisted transport and interfacial barrier preservation, contributing to at least a 100 times enhancement in RR, NL, and the on/off ratio. These findings establish a systematic understanding of SRM performance modulation mechanisms and provide a rational design approach for selector-free memristive devices toward scalable CIM applications.
Authors
- Renhui Jiang (ORCID: https://orcid.org/0000-0003-1109-2142)
- Huajun Sun (ORCID: https://orcid.org/0000-0003-0755-5247)
- Wenbin Zuo (ORCID: https://orcid.org/0000-0001-7815-0218)
- Xiao Dong Fu (ORCID: https://orcid.org/0000-0002-0976-220X)
- 易运晖 (ORCID: https://orcid.org/0009-0009-4260-0817)
- Yi Li (ORCID: https://orcid.org/0000-0003-1930-8550)
- Xiangshui Miao (ORCID: https://orcid.org/0000-0002-3999-7421)
- Yi-Bai Xue (ORCID: https://orcid.org/0009-0004-3077-8486)
- Zirui Li (ORCID: https://orcid.org/0009-0002-7146-119X)
- Hengfeng Zhang (ORCID: https://orcid.org/0009-0009-8969-3571)
Institutions
- Chinese Academy of Sciences (CN)
- Shanghai Institute of Technical Physics (CN)
- State Key Laboratory of Infrared Physics (CN)
- Hubei Jiufengshan Laboratory (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2026-11-09
- DOI
- https://doi.org/10.1063/5.0356795
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hubei Province