Enhanced resistance switching ratio of Hf0.5Zr0.5O2-based film through reducing oxygen vacancy concentration

The resistance switching mechanism in HfO2-based resistive random access memory (RRAM) typically involves conductive filaments formed by oxygen vacancies. The inherent characteristics of this mechanism lead to low reliability and elevated off-state current in HfO2-based RRAM devices. Herein, the oxygen vacancy concentration in Hf0.5Zr0.5O2 film was decreased through the acceptor (La3+)–donor (Ta5+) co-doping strategy, which reduced off-state current from 7.6 × 10−3 A/cm2 to 3.6 × 10−6 A/cm2 at 0.5 V, increased the resistance switching ratio from 6.2 × 102 to 2.4 × 104, and improved switching stability. Following the doping of Hf0.5Zr0.5O2 film with La3+ and Ta5+, the resistance switching mechanism changes from oxygen vacancy conductive filaments to the electron capture/de-capture mechanism. The conduction mechanism of the high resistance state transitions from Schottky emission to space charge limited current, while that of the low resistance state transitions from Ohmic conduction to Fowler–Nordheim tunneling. X-ray photoelectron spectroscopy has shown that the co-doping approach decreases the concentration of oxygen vacancies, which in turn influences the alterations in the resistance switching performances and mechanisms. This study presents an effective approach for the design and improvement of HfO2-based RRAM, while also advancing the comprehension of the resistance switching mechanism influenced by doping in HfO2-based films.

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Publication Details

Journal
Applied Physics Letters
Published
2025-09-29
DOI
https://doi.org/10.1063/5.0285927
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Enhanced resistance switching ratio of Hf0.5Zr0.5O2-based film through reducing oxygen vacancy concentration

Chun Feng, Jun Miao, Yang Bai, Jin Peng Cao et al.
Applied Physics Letters
Advanced Memory and Neural Computing
article

Enhanced resistance switching ratio of Hf0.5Zr0.5O2-based film through reducing oxygen vacancy concentration

Chun Feng, Jun Miao, Yang Bai, Jin Peng Cao, Li Hong Yang, Hong Wei Wang, Qiliang Li, Wei Deng, Zhou Zhang, Jun Kun Wu
article en

Abstract

The resistance switching mechanism in HfO2-based resistive random access memory (RRAM) typically involves conductive filaments formed by oxygen vacancies. The inherent characteristics of this mechanism lead to low reliability and elevated off-state current in HfO2-based RRAM devices. Herein, the oxygen vacancy concentration in Hf0.5Zr0.5O2 film was decreased through the acceptor (La3+)–donor (Ta5+) co-doping strategy, which reduced off-state current from 7.6 × 10−3 A/cm2 to 3.6 × 10−6 A/cm2 at 0.5 V, increased the resistance switching ratio from 6.2 × 102 to 2.4 × 104, and improved switching stability. Following the doping of Hf0.5Zr0.5O2 film with La3+ and Ta5+, the resistance switching mechanism changes from oxygen vacancy conductive filaments to the electron capture/de-capture mechanism. The conduction mechanism of the high resistance state transitions from Schottky emission to space charge limited current, while that of the low resistance state transitions from Ohmic conduction to Fowler–Nordheim tunneling. X-ray photoelectron spectroscopy has shown that the co-doping approach decreases the concentration of oxygen vacancies, which in turn influences the alterations in the resistance switching performances and mechanisms. This study presents an effective approach for the design and improvement of HfO2-based RRAM, while also advancing the comprehension of the resistance switching mechanism influenced by doping in HfO2-based films.

Applied Physics LettersVol. 127(13)
Chinese Academy of Sciences (CN), Peking University (CN), Beijing University of Technology (CN), Institute of Solid State Chemistry (RU), Robotics Research (United States) (US), FZU ‒ Institute of Physics of the Academy of Sciences of the Czech Republic (CZ), Division of Materials Science and Engineering (AU), Institute of Physics (CN), University of Science and Technology Beijing (CN)
Openalex Percentile: Top 74%
Advanced Memory and Neural Computing
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