Approaching quantum anomalous Hall effect in proximity-coupled YIG/graphene/h-BN sandwich structure

Tang, Chi; Cheng, Bin; Aldosary, Mohammed; Wang, Zhiyong; Jiang, Zilong; Watanabe, Kenji; Taniguchi, Takashi; Bockrath, Marc

doi:10.1063/1.5001318

Cited by 45 publications

(51 citation statements)

References 27 publications

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“…To this end, initial efforts were made to induce magnetism in graphene by interfacing it with yttrium iron garnet (YIG), an insulator that is ferromagnetic up to 560 K [24]. Charge transport measurements have suggested the presence of a perpendicular exchange field induced in the graphene layer via proximity to YIG [25][26][27]. However, spin transport measurements indicate that while in-plane ferromagnetism can be induced in the graphene layer, the out-of-plane exchange field remains purely paramagnetic and thus disappears in the absence of an external magnetic field [28].…”

Section: Introductionmentioning

confidence: 99%

Probing magnetism via spin dynamics in graphene/2D-ferromagnet heterostructures

Cummings¹

2019

J. Phys. Mater.

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The recent discovery of two-dimensional magnetic insulators has generated a great deal of excitement over their potential for nanoscale manipulation of spin or magnetism. One intriguing use for these materials is to put them in contact with graphene, with the goal of making graphene magnetic while maintaining its unique electronic properties. Such a system could prove useful in applications such as magnetic memories, or could serve as a host for exotic states of matter. Proximity to a magnetic insulator will alter the spin transport properties of graphene, and the strength of this interaction can be probed with Hanle spin precession experiments. To aid in the analysis of such experiments, in this work we derive an explicit expression for Hanle spin precession in graphene interfaced with a ferromagnetic insulator whose magnetization points perpendicular to the graphene plane. We find that this interface results in a shifted and asymmetric Hanle response, and we discuss how this behavior can be used to interpret measurements of spin transport in these systems.

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Section: Introductionmentioning

confidence: 99%

Probing magnetism via spin dynamics in graphene/2D-ferromagnet heterostructures

Cummings¹

2019

J. Phys. Mater.

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“…The sublattice imbalance could arise for a graphene monolayer deposited on boron nitride in a commensurate fashion [11,60]. The Rashba and exchange fields naturally arise for a graphene monolayer deposited over a ferromagnetic insulator, such as YIG [61,62], EuO [63], or CrI 3 [64,65]. Furthermore, the noncommensuration of graphene with the substrate creates Moire patterns, resulting in an effective spatial modulation of the different contributions [9][10][11].…”

Section: B Two-dimensional Chern Insulatormentioning

confidence: 99%

Real-space mapping of topological invariants using artificial neural networks

et al. 2018

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Topological invariants allow one to characterize Hamiltonians, predicting the existence of topologically protected in-gap modes. Those invariants can be computed by tracing the evolution of the occupied wave functions under twisted boundary conditions. However, those procedures do not allow one to calculate a topological invariant by evaluating the system locally, and thus require information about the wave functions in the whole system. Here we show that artificial neural networks can be trained to identify the topological order by evaluating a local projection of the density matrix. We demonstrate this for two different models, a one-dimensional topological superconductor and a two-dimensional quantum anomalous Hall state, both with spatially modulated parameters. Our neural network correctly identifies the different topological domains in real space, predicting the location of in-gap states. By combining a neural network with a calculation of the electronic states that uses the kernel polynomial method, we show that the local evaluation of the invariant can be carried out by evaluating a local quantity, in particular for systems without translational symmetry consisting of tens of thousands of atoms. Our results show that supervised learning is an efficient methodology to characterize the local topology of a system.

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“…Moreover, the proximity effect can also be deliberately exploited to specifically tune the properties of a graphene sheet [14,15,16,17,18]. For example, the almost negligible spin-orbit coupling in graphene can be significantly increased by bringing the graphene layer into contact with transition metal dichalcogenides [14,15].…”

Section: Introductionmentioning

confidence: 99%

Substrate induced nanoscale resistance variation in epitaxial graphene

et al. 2020

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Graphene, the first true two-dimensional material still reveals the most remarkable transport properties among the growing class of two-dimensional materials. Although many studies have investigated fundamental scattering processes, the surprisingly large variation in the experimentally determined resistances associated with a localized defect is still an open issue. Here, we quantitatively investigate the local transport properties of graphene prepared by polymer assisted sublimation growth (PASG) using scanning tunneling potentiometry. PASG graphene is characterized by a spatially homogeneous current density, which allows to analyze variations in the local electrochemical potential with high precision. We utilize this possibility by examining the local sheet resistance finding a significant variation of up to 270% at low temperatures. We identify a correlation of the sheet resistance with the stacking sequence of the 6H-SiC substrate as well as with the distance between the graphene

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Approaching quantum anomalous Hall effect in proximity-coupled YIG/graphene/h-BN sandwich structure

Cited by 45 publications

References 27 publications

Probing magnetism via spin dynamics in graphene/2D-ferromagnet heterostructures

Probing magnetism via spin dynamics in graphene/2D-ferromagnet heterostructures

Real-space mapping of topological invariants using artificial neural networks

Substrate induced nanoscale resistance variation in epitaxial graphene

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