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非共线反铁磁体MnGe中的非常规自旋电流

Unconventional Spin Currents in Noncollinear Antiferromagnet MnGe.

作者信息

Cao Cuimei, Chen Shiwei, Xie Nian, Chen Jinsen, Guo Zhe, Zhang Hui, Li Zheng, Wang Yihan, Qiu Xuepeng, Liang Shiheng, Zhan Qingfeng, You Long

机构信息

School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

School of Physics, Hubei University, Wuhan 430062, China.

出版信息

Nano Lett. 2025 Jun 11;25(23):9477-9484. doi: 10.1021/acs.nanolett.5c02185. Epub 2025 Jun 2.

DOI:10.1021/acs.nanolett.5c02185
PMID:40455593
Abstract

Unconventional spin-orbit torque (SOT) has garnered significant attention due to its potential for enabling energy-efficient and deterministic control of magnetization states. Noncollinear antiferromagnets (NCAFs) are considered ideal systems for generating such unconventional SOTs, making them highly promising for the development of next-generation spintronic devices. In this study, we report the generation of spin torques in noncollinear antiferromagnetic MnGe, revealing the presence of unconventional spin polarizations (specifically, - and -polarized spin currents) by employing the spin-torque ferromagnetic resonance (ST-FMR) technique. All-electric SOT switching was successfully demonstrated in a MnGe-based device with perpendicular magnetic anisotropy (PMA), with a critical current density of 4.2 × 10 A/cm. Furthermore, the device exhibits memristive behavior that effectively emulates the functionality of artificial synapses in convolutional neural networks (CNNs), achieving an accuracy of 92.5% in digital recognition tasks. These results are expected to pave the way toward next-generation fast and energy-efficient memory and neuromorphic computing.

摘要

非传统自旋轨道转矩(SOT)因其在实现磁化状态的节能和确定性控制方面的潜力而备受关注。非共线反铁磁体(NCAFs)被认为是产生这种非传统SOT的理想系统,这使其在下一代自旋电子器件的发展中极具前景。在本研究中,我们报告了在非共线反铁磁体MnGe中产生自旋转矩,通过采用自旋转矩铁磁共振(ST-FMR)技术揭示了非传统自旋极化(具体而言,-和-极化自旋电流)的存在。在具有垂直磁各向异性(PMA)的基于MnGe的器件中成功演示了全电SOT切换,临界电流密度为4.2×10 A/cm。此外,该器件表现出忆阻行为,有效地模拟了卷积神经网络(CNN)中人工突触的功能,在数字识别任务中实现了92.5%的准确率。这些结果有望为下一代快速且节能的存储器和神经形态计算铺平道路。

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