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arXiv 2608.11615cond-mat.mes-hallcond-mat.mtrl-sciphysics.comp-phquant-ph

SiO₂界面诱导的石墨烯自旋寿命各向异性

Spin lifetime anisotropy in graphene induced by the SiO2 interface

Aron W. Cummings, Chunhao Guo, Andrew Grieder, Shihao Tu, Mayank Gupta, Junqing Xu, Juan Marmolejo-Tejada, Yuan Ping

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中文总结 AI 辅助

该研究通过数值模拟揭示SiO₂衬底诱导石墨烯产生自旋寿命各向异性,其结果为解释相关实验及指导石墨烯自旋电子学的衬底工程提供了关键依据。

中文摘要 AI 辅助

理解常见介电衬底如何影响石墨烯的自旋输运特性,对推进基于石墨烯的自旋电子技术至关重要。本文采用一套全面的数值模拟方法,揭示SiO₂衬底如何改变石墨烯的自旋结构并调控其自旋弛豫。通过第一性原理密度矩阵动力学模拟以及紧束缚(TB)输运模拟,我们量化了电子-声子散射、杂质散射和静电无序对自旋弛豫过程的影响。研究发现,二维SiO₂衬底在石墨烯中诱导出以Rashba型为主的螺旋自旋结构,导致自旋寿命各向异性为1/2;同时,体相SiO₂会破坏石墨烯的面内对称性,使自旋结构产生各向异性的面内和面外分量,我们通过新开发的石墨烯紧束缚模型捕捉到了这一现象。在实际无序条件下的输运模拟显示,自旋寿命各向异性介于0.5至1之间,与SiO₂衬底上石墨烯自旋阀的测量结果相似。我们的结果揭示了普遍存在的石墨烯/SiO₂界面处自旋弛豫的复杂图像,超出了标准Rashba模型的范畴,为解释实验结果和指导石墨烯自旋电子学的衬底工程提供了关键见解。

英文摘要

Understanding how common dielectric substrates influence the spin transport properties of graphene is essential for advancing graphene-based spintronic technologies. Here we use a comprehensive set of numerical simulations to reveal how a SiO$_2$ substrate modifies the spin texture and governs spin relaxation in graphene. Using first-principles density matrix dynamics simulations, as well as tight-binding (TB) transport simulations, we quantify the effects of electron-phonon scattering, impurity scattering, and electrostatic disorder on the spin relaxation process. We find that a 2D SiO$_2$ substrate induces a predominantly Rashba-type helical spin texture in graphene, leading to a spin lifetime anisotropy of 1/2. Meanwhile, bulk SiO$_2$ breaks in-plane symmetry in graphene, leading to anisotropic in-plane and out-of-plane components in the spin texture, which we capture with a newly-developed TB model of graphene. Transport simulations under realistic disorder conditions reveal a spin lifetime anisotropy between 0.5 and 1, similar to what is seen in measurements of graphene spin valves on a SiO$_2$ substrate. Our results reveal a more complex picture of spin relaxation at the ubiquitous graphene/SiO$_2$ interface, beyond the standard Rashba model, providing critical insight for interpreting experiments and guiding substrate engineering for graphene spintronics.

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