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Interfacial electronic band-matching (EBM) plays a crucial role in determining the spin-dependent transport properties and performance of spintronic devices. The final goal of this study is to establish a method to search for new material combinations that exhibit favorable EBM at the interfaces to achieve a superior performance in various spintronic devices using the machine learning technique combined with the first-principles calculations. As a first step, we investigate the effect of interfacial EBM on magnetoresistance (MR) by fabricating the currentin- plane giant magnetoresistive devices with compositionally graded Co1−xFex layers and Cu spacer. The MR ratio varies significantly across x = 0.11–1.0, with the highest MR of 17.5% observed at x = 0.46, followed by a sharp decrease beyond x = 0.6. To analyze the x dependence of MR in terms of EBM with low computational cost, we calculate the simple Fermi surfaces of bcc Co1−xFex and Cu and evaluate the wave
number (k) distance between their Fermi surfaces. The closest (furthest) Fermi surface match occurs at x = 0.4 (1.0), which tends to be in good agreement with the observed MR trend. This suggests that a simple Fermi surface similarity analysis, when integrated

権利情報:

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International
  

キーワード: spintronics, giant magnetoresistance, machine learning, Fermi surface, interfacial electronic band matching

刊行年月日: 2024-12-01

出版者: AIP Publishing

掲載誌:

• AIP Advances (ISSN: 21583226) vol. 14 issue. 12 125329

研究助成金:

• Japan Society for the Promotion of Science 19K05249
• Core Research for Evolutional Science and Technology JPMJCR21O1
• MEXT Program JPMXP1122715503

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1063/5.0216909

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更新時刻: 2025-01-07 16:30:50 +0900

MDRでの公開時刻: 2025-01-07 16:30:50 +0900