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.

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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

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article

Engineering oxygen vacancies via Ar+ ion implantation in single-layer HfO2 self-rectifying memristors

Renhui Jiang, Huajun Sun, Wenbin Zuo, Xiao Dong Fu et al.
7 links
Applied Physics Letters
Advanced Memory and Neural Computing
article

Engineering oxygen vacancies via Ar+ ion implantation in single-layer HfO2 self-rectifying memristors

Renhui Jiang, Huajun Sun, Wenbin Zuo, Xiao Dong Fu, 易运晖, Yi Li, Xiangshui Miao, Yi-Bai Xue, Zirui Li, Hengfeng Zhang
article en

Abstract

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.

Applied Physics LettersVol. 129(19)
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)
National Natural Science Foundation of China, Natural Science Foundation of Hubei Province
Advanced Memory and Neural Computing
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