Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNonreciprocal Dzyaloshinskii-Moriya magnetoacoustic waves
152
0
0.0
(
0
)
Added by
Matthias K\\\"u{\\ss}
Publication date
2020
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We study the interaction of surface acoustic waves with spin waves in ultra-thin CoFeB/Pt bilayers. Due to the interfacial Dzyaloshinskii-Moriya interaction (DMI), the spin wave dispersion is non-degenerate for oppositely propagating spin waves in CoFeB/Pt. In combination with the additional nonreciprocity of the magnetoacoustic coupling itself, highly nonreciprocal acoustic wave transmission through the magnetic film is observed. We systematically characterize the magnetoacoustic wave propagation in a thickness series of CoFeB($d$)/Pt samples as a function of magnetic field magnitude and direction, and at frequencies up to 7 GHz. We quantitatively model our results to extract the strength of the DMI and magnetoacoustic driving fields.
rate research
Read More
In thin magnetic layers with structural inversion asymmetry and spin-orbit coupling, a Dzyaloshinskii-Moriya interaction arises at the interface. When a spin wave current ${bf j}_m$ flows in a system with a homogeneous magnetization {bf m}, this interaction produces an effective field-like torque on the form ${bf T}_{rm FL}propto{bf m}times({bf z}times{bf j}_m)$ as well as a damping-like torque, ${bf T}_{rm DL}propto{bf m}times[({bf z}times{bf j}_m)times{bf m}]$ in the presence of spin-wave relaxation (${bf z}$ is normal to the interface). These torques mediated by the magnon flow can reorient the time-averaged magnetization direction and display a number of similarities with the torques arising from the electron flow in a magnetic two dimensional electron gas with Rashba spin-orbit coupling. This magnon-mediated spin-orbit torque can be efficient in the case of magnons driven by a thermal gradient.
The Dzyaloshinskii-Moriya interaction (DMI), being one of the origins for chiral magnetism, is currently attracting huge attention in the research community focusing on applied magnetism and spintronics. For future applications an accurate measurement of its strength is indispensable. In this work, we present a review of the state of the art of measuring the coefficient $D$ of the Dzyaloshinskii-Moriya interaction, the DMI constant, focusing on systems where the interaction arises from the interface between two materials. The measurement techniques are divided into three categories: a) domain wall based measurements, b) spin wave based measurements and c) spin orbit torque based measurements. We give an overview of the experimental techniques as well as their theoretical background and models for the quantification of the DMI constant $D$. We analyze the advantages and disadvantages of each method and compare $D$ values in different stacks. The review aims to obtain a better understanding of the applicability of the different techniques to different stacks and of the origin of apparent disagreement of literature values.
We examine the current-induced dynamics of a skyrmion that is subject to both structural and bulk inversion asymmetry. There arises a hybrid type of Dzyaloshinskii-Moriya interaction (DMI) which is in the form of a mixture of interfacial and bulk DMIs. Examples include crystals with symmetry classes C$_n$ as well as magnetic multilayers composed of a ferromagnet with a noncentrosymmetric crystal and a nonmagnet with strong spin-orbit coupling. As a striking result, we find that, in systems with a hybrid DMI, the spin-orbit-torque-induced skyrmion Hall angle is asymmetric for the two different skyrmion polarities ($pm 1$ given by out-of-plane core magnetization), even allowing one of them to be tuned to zero. We propose several experimental ways to achieve the necessary straight skyrmion motion (with zero Hall angle) for racetrack memories, even without antiferromagnetic interactions or any interaction with another magnet. Our results can be understood within a simple picture by using a global spin rotation which maps the hybrid DMI model to an effective model containing purely interfacial DMI. The formalism directly reveals the effective spin torque and effective current that result in qualitatively different dynamics. Our work provides a way to utilize symmetry breaking to eliminate detrimental phenomena as hybrid DMI eliminates the skyrmion Hall angle.
Dzyaloshinskii-Moriya interaction (DMI) is vital to form various chiral spin textures, novel behaviors of magnons and permits their potential applications in energy-efficient spintronic devices. Here, we realize a sizable bulk DMI in a transition metal dichalcogenide (TMD) 2H-TaS2 by intercalating Fe atoms, which form the chiral supercells with broken spatial inversion symmetry and also act as the source of magnetic orderings. Using a newly developed protonic gate technology, gate-controlled protons intercalation could further change the carrier density and intensely tune DMI via the Ruderman-Kittel-Kasuya-Yosida mechanism. The resultant giant topological Hall resistivity of 1.4 uohm.cm at -5.2V (about 460% of the zero-bias value) is larger than most of the known magnetic materials. Theoretical analysis indicates that such a large topological Hall effect originates from the two-dimensional Bloch-type chiral spin textures stabilized by DMI, while the large anomalous Hall effect comes from the gapped Dirac nodal lines by spin-orbit interaction. Dual-intercalation in 2HTaS2 provides a model system to reveal the nature of DMI in the large family of TMDs and a promising way of gate tuning of DMI, which further enables an electrical control of the chiral spin textures and related electromagnetic phenomena.
Machine learning is applied to a large number of modern devices that are essential in building 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.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
