Electrodeposited <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Co</mml:mi></mml:math>-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Ni</mml:mi></mml:math> layered double hydroxid
With the rapid development of data science, higher performance hardware, such as resistive-switching memory with alternative ultrathin materials, emerges. Although the dielectric constant is an important parameter for photoelectric devices, its tunability in a thin film is needed, a method for its measurement in an ultrathin film is lacking, and its relationship with resistive-switching behavior is rarely investigated. Here, a dielectric-property-tunable thickness-controllable continuous $\mathrm{Co}$-$\mathrm{Ni}$ layered double hydroxide (LDH) film is achieved by bulk-phase doping of polyvinyl alcohol (PVA) using an electrodeposition method, and a general method for dielectric constant measurement of a thin film based on a parallel-plate capacitor is suggested. The $\mathrm{Co}$-$\mathrm{Ni}$ LDH-PVA significantly suppresses dielectric loss by 99%, and an enhanced resistive-switching performance is achieved with an increased initial resistivity, a suppressed leakage current, a reduced set power by 79%, a data retention over ${10}^{4}$ s, an endurance up to 200 cycles, and a synapselike capability, compared with pure LDH material. The enhanced electrical performance comes from an improved inherent trade-off of dielectric constants inside an electrodeposited LDH film that is tuned by PVA; this is supported by density-functional calculations. This work provides a reference for the selection and modification of resistive-switching materials in relation to dielectric constants.
Authors
- Min Qian (ORCID: https://orcid.org/0000-0002-6472-7693)
- Yueping Niu (ORCID: https://orcid.org/0000-0003-1404-930X)
- Y. K. Zhang
- Yang Gao (ORCID: https://orcid.org/0000-0002-1069-2157)
- Shangqing Gong
- Chang Xi
- Cheng Qian (ORCID: https://orcid.org/0000-0002-5502-0303)
- Xiaoyang Xuan (ORCID: https://orcid.org/0000-0003-0599-1862)
- Xiaojun Mao
Institutions
- East China University of Science and Technology (CN)
- Taishan University (CN)
Publication Details
- Journal
- Physical Review Applied
- Published
- 2025-05-05
- DOI
- https://doi.org/10.1103/physrevapplied.23.054010
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Natural Science Foundation of China
- Shanghai Municipal Education Commission