Antiferromagnetic materials have at least two spin sublattices generally aligned antiparallel to each other so that the net magnetization vanishes. Although they were originally considered “interesting yet useless”, metallic antiferromagnets have found their first applications in magnetic tunnel junctions as an exchange layer for strengthening the hard ferromagnetic layer and thereby establishing a reference magnetization direction. More recently they have been identified as materials with a more direct role in spintronics, in particular for storing data in information technologies. The advantage of antiferromagnetic materials is their zero net moment, so the packing density of the storing elements can be much higher than for simple ferromagnetic storing elements. Besides, antiferromagnets require very large magnetic field to manipulate their magnetic structure, so they are not easily erasable. Another advantage is the high frequency of magnetization dynamics which may find potential application for THz wave generators.
Our group focuses on the spin current injection to and from antiferromagnetic materials using charge current, heat and light for spintronic applications. We are also interested in the ultra-fast dynamics of antiferromagnetic materials resulting from their high exchange energy between the two sublattices. One of our focus is to excite the magnon modes in these materials using the spin current from the adjacent layers. Besides, we are interested in novel antiferromagnetic materials, their exotic physical phenomena and their spin manipulation.
Our vision on the current research challenges for Metallic Antiferromagnets can be found in the following Perspective article:
Saima A. Siddiqui et al., J. Appl. Phys. 128, 040904 (2020).
Jose Holanda et al., Phys. Rev. Lett. 124, 117202 (2020).
Jakub Železný et al., Nature Phys. 14, 220 (2018).
Hilal Saglam et al., Phys. Rev. B 94, 140412(R) (2016).
Stephen M. Wu et al., Phys. Rev. Lett. 116, 097204 (2016).
Wei Zhang et al., Phys. Rev. Lett. 113, 196602 (2014).
Novel Materials for Neuromorphic Computing
Neuromorphic computing refers to a class of computation strategies that relies on neuron-inspired architecture. The brain is known to be an exceedingly powerful learning computer well-suited to fuzzy tasks such as linguistic translation or image recognition, and by emulating the structure of neurons, we aim to artificially replicate that functionality. Unfortunately, traditional computer hardware relies on linear computation components, whereas neurons operate in a non-linear fashion. Thus we need a completely new architecture if we wish to push past the computational limits looming on the horizon of traditional computing.
One promising candidate for a new artificial neuron is a spin torque resonator, a magnetic layer that can be driven into a resonant state by applied spin torques. It has been demonstrated that such devices can be used to great effect for speech recognition, but many challenges must still be overcome to make spin torque resonators practical. The most fundamental of these issues is the need for an efficient and low-power spin source that generates torques well-suited to the task of driving magnets into resonance. Once this is done, we must still demonstrate a practical and scalable implementation of these devices. Our research approaches both of these challenges, but we are initially focused on the first, as better materials will enable easier and more efficient implementations. We have thus far examined three materials in particular for their potential.
We have characterized the spin properties of several materials using techniques such as spin torque ferromagnetic resonance and DC transport measurements. At the moment, we are focused on materials with magnetic ordering, as well as ultrathin materials. In the future aim to characterize more materials, with a focus on materials with quantum properties that enable interesting and efficient spin torque generation. Our future plans for this project include creating practical devices using iron rhodium or another promising material as the spin source and demonstrating functional oscillator control, as well as pursuing other interesting materials that might have high spin torque efficiencies well-suited to neuromorphic computing.
Yi Li et al., Phys. Rev. Lett. 124, 117202 (2020).
Magnetic skyrmions are topologically protected chiral spin textures which do not only exhibit fundamentally new physics, but also possess the potential to be exploited in future data storage and information technology applications, such as so-called racetrack memories. While the very first experimental works had demonstrated the existence of skyrmion phases at low temperatures in bulk chiral magnets, it was later shown that these topological quasiparticles can also be stabilized at room temperature in systems like thin films or magnetic multilayers. Moreover, skyrmions in (synthetic) ferri- and antiferromagnets were demonstrated to exhibit superior properties in comparison to their ferromagnetic counterparts; mainly owing to enhanced propagation velocities due to the suppression of the skyrmion Hall effect.
Over the past years, our group’s research has focused on issues like the electrical generation of magnetic skyrmions or the influence of topology on the dynamic behavior of these magnetic objects. Central results include the creation of room-temperature skyrmions by pushing elongated magnetic domains through a geometrical constriction with electric currents (see publications listed further below), as well as an improved understanding of the skyrmion Hall effect.
More recently, our research group aims to contribute to a better understanding of skyrmions in ferri- and antiferromagnetic multilayers and to design prototypical device architectures for future memory and information processing solutions. Currently, we are interested in the dynamic (GHz-range) eigen-excitations of magnetic skyrmions. Furthermore, we investigate static and dynamic properties of skyrmions in magnetic multilayers of different composition. Besides micromagnetic simulations, we utilize broadband microwave absorption spectroscopy, spin-torque ferromagnetic resonance and spatially-resolved Brillouin light scattering spectroscopy to study these chiral magnetic textures.
Wanjun Jiang et al., Phys. Rep. 704, 1 (2017).
Wanjun Jiang et al., Nature Phys. 13, 162 (2017).
Wanjun Jiang et al., Science 349, 283 (2015).
Magnons (or spin waves) are the fundamental excitation quanta of magnetically ordered systems. Their properties are determined by exchange interactions, magnetic anisotropies, and dipolar interactions, where the latter can be tuned via sample geometry. Together with the inherently strong non-linearities of magnetization dynamics and the strong coupling of spin to other degrees of freedom, such as photons, electrons, and phonons this enables a wide range of possibilities to control and manipulate magnon behavior on purpose. In particular, by targeted design of magnetic materials we can tailor the magnon bandstructures, which may even enable topologically non-trivial chiral magnonic edge states. In addition, the actual magnonic bandstructure may become reprogrammable by establishing different metastable magnetization configurations.
Our research focuses on purposefully engineering magnon phenomena with the goal to use their unique properties for functional devices. A particular interesting pathway for manipulating magnons is the fact that can form hybrid polariton modes via strong coupling to other excitation, in particular phonons, photons, and other magnons. This opens up new possibilities for converting quantum information coherently from one excitation to another, and therefore may play a key role in distributed quantum information systems. Furthermore, magnons can form Bose-Einstein condensates, which are governed by classical dynamics, but are regulated via a delicate quantum balance. We want to explore this boundary between quantum and classical behavior and exploit it for new physics and novel functionality. This work on engineered quantum magnonic systems may help to develop new computational and logic systems with minimal energy dissipation, as well as novel ultra-high sensitivity sensors.
Yi Li et al., Phys. Rev. Lett. 124, 117202 (2020).
Yi Li et al., Phys. Rev. Lett. 123, 107701 (2019).