4.3 Article

In situ manipulation of perpendicular magnetic anisotropy in half-metallic NiCo2O4 thin film by proton insertion

期刊

JAPANESE JOURNAL OF APPLIED PHYSICS
卷 61, 期 SM, 页码 -

出版社

IOP Publishing Ltd
DOI: 10.35848/1347-4065/ac594f

关键词

nanoionics; magnetic anisotropy; proton insertion; solid electrolyte; In situ manipulation; solid state ionics

资金

  1. Japan Society for the Promotion of Science (JSPS) KAKENHI [JP20H05301, 19H05816, 19K05279, 21H01810, 20H00397, 19H05823]
  2. JSPS Core-to-Core Program (A) Advanced Research Networks
  3. NIMS Electron Microscopy Analysis Station, Nanostructural Characterization Group
  4. Grants-in-Aid for Scientific Research [19K05279, 21H01810] Funding Source: KAKEN

向作者/读者索取更多资源

In this study, we report a method to manipulate perpendicular magnetic anisotropy (PMA) using an all-solid-state redox device. By utilizing a proton-conducting solid electrolyte, the PMA of a NiCo2O4 thin film was successfully manipulated at low voltage. This method enables various reservoir states and contributes to lower power consumption and high-precision machine learning.
Herein we report the manipulation of perpendicular magnetic anisotropy (PMA), a very important technique for spintronics devices, which is achieved in an all-solid-state redox (reduction/oxidation) device. The device consisted of NiCo2O4 (NCO) thin film with a c-axis orientation and a proton-conducting polymer (Nafion). The PMA of NCO can be manipulated in situ with low voltage (V <= 0.7 V) due to the utilization of a proton-conducting solid electrolyte. The magnetic anisotropy variation was quantitatively evaluated by an anomalous Hall measurement. The magnetic anisotropy field of the NCO thin film was successfully manipulated in the range of 5%, although the rotation of the easy axis of magnetization was not observed. This modulation is attributed to the strain and electronic configuration variation by proton insertion. This manipulation method for PMA with the solid electrolyte enables a variety of reservoir states and contributes to lower power consumption and high-precision machine learning.

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