Measuring interfacial Dzyaloshinskii-Moriya interaction in ultra-thin magnetic films

Kuepferling, Michaela; Casiraghi, Arianna; Soares, Gabriel Vieira; Durin, Gianfranco; García-Sánchez, F.; Chen, Liu; Back, Christian; Marrows, Christopher H.; Tacchi, S.; Carlotti, G.

doi:10.48550/arxiv.2009.11830

Cited by 11 publications

(14 citation statements)

References 195 publications

(435 reference statements)

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“…We find shows a sharp increase as d exceeds ~1 nm. We consider the texture of the Pt/Co interface plays a dominant role in defining D. The size of D when d exceeds ~1 nm is in agreement with past reports 20,[34][35][36] (see Supplementary Table 1 for values of D obtained in similar heterostructures).…”

Section: Magnetic Properties Of the Samples For Pattern Recognitionsupporting

confidence: 89%

Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning

et al. 2021

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Machine learning is applied to a large number of modern devices that are essential in building an energy-efficient smart society. Audio and face recognition are among the most well-known technologies that make use of such artificial intelligence. In materials research, machine learning is adapted to predict materials with certain functionalities, an approach often referred to as materials informatics. Here, we show that machine learning can be used to extract material parameters from a single image obtained in experiments. The Dzyaloshinskii–Moriya (DM) interaction and the magnetic anisotropy distribution of thin-film heterostructures, parameters that are critical in developing next-generation storage class magnetic memory technologies, are estimated from a magnetic domain image. Micromagnetic simulation is used to generate thousands of random images for training and model validation. A convolutional neural network system is employed as the learning tool. The DM exchange constant of typical Co-based thin-film heterostructures is studied using the trained system: the estimated values are in good agreement with experiments. Moreover, we show that the system can independently determine the magnetic anisotropy distribution, demonstrating the potential of pattern recognition. This approach can considerably simplify experimental processes and broaden the scope of materials research.

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Section: Magnetic Properties Of the Samples For Pattern Recognitionsupporting

confidence: 89%

Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning

et al. 2021

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“…The impact of the interfacial DMI | | on all but the surface spin waves decreases with the magnetic film thickness [234,235]. For thin ferromagnetic films in contact with heavy metals typically ∼ 1 mJ∕m 2 (techniques for measuring and its values for different materials have been reviewed in [236]). The DMI also favors out-of-plane magnetization since the DMI vector is perpendicular to the film normal and when strong enough may pull the magnetization out of the film plane (refer to Sec.…”

Section: Chiral Spin Waves By Spin-orbit Interactionmentioning

confidence: 99%

Chirality as Generalized Spin-Orbit Interaction in Spintronics

Chen¹,

Luo²,

Bauer³

2022

Preprint

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Chirality or handedness is a digital relation between three vectors that distinguishes an object from its mirror image, such as the spread fingers of the right and left hand. The chirality of ground state magnetic textures defined by the vectors of magnetization, its gradient, and an electric field from broken inversion symmetry can be fixed by a strong relativistic spin-orbit interaction. This review focuses on the chirality observed in the excited states of the magnetic order, dielectrics, and conductors that hold transverse spins when they are evanescent. Even without any relativistic effect, the transverse spin of the evanescent waves are locked to the momentum and the surface normal of their propagation plane. This chirality thereby acts as a generalized spin-orbit interaction, which leads to the discovery of various chiral interactions between magnetic, phononic, electronic, photonic, and plasmonic excitations in spintronics that mediate the excitation of quasiparticles into a single direction, leading to phenomena such as chiral spin and phonon pumping, chiral spin Seebeck, spin skin, magnonic trap, magnon Doppler, and spin diode effects. Intriguing analogies with electric counterparts in the nano-optics and plasmonics exist. After a brief review of the concepts of chirality that characterize the ground state chiral magnetic textures and chirally coupled magnets in spintronics, we turn to the chiral phenomena of excited states. We present a unified electrodynamic picture for dynamical chirality in spintronics in terms of generalized spin-orbit interaction and compare it with that in nano-optics and plasmonics. Based on the general theory, we subsequently review the theoretical progress and experimental evidence of chiral interaction, as well as the near-field transfer of the transverse spins, between various excitations in magnetic, photonic, electronic and phononic nanostructures at GHz time scales. We provide a perspective for future research before concluding this article.

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“…Refs. [46][47][48][49][50][51]). In thin films DMI originating from strong SOC of interfacial atoms neighboring the magnetic layer can be engineered and controlled.…”

Section: Bmentioning

confidence: 99%

Theory of surface-induced multiferroicity in magnetic materials, thin films, and multilayers

2021

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We present a theoretical study of the onset of electric polarization close to a surface in magnetic materials and in thin films and multilayers. We consider two different paths that lead to the onset of multiferroic behavior at the boundary in materials that are bulk collinear ferromagnets or antiferromagnets. These two paths are distinguished by the presence or absence of a surface induced Dzyaloshinskii-Moriya interaction which can be taken into account through Lifshitz invariants in the free energy of the system. Experimental consequences are discussed in the light of the theory.

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Measuring interfacial Dzyaloshinskii-Moriya interaction in ultra-thin magnetic films

Cited by 11 publications

References 195 publications

Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning

Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning

Chirality as Generalized Spin-Orbit Interaction in Spintronics

Theory of surface-induced multiferroicity in magnetic materials, thin films, and multilayers

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