Tohoku University

Tohoku University researchers have observed an opto-magnetic torque approximately five times more efficient than in conventional magnets. This breakthrough could be extremely beneficial for the development of light-based spin memory and storage technologies.

Opto-magnetic torque is a method which can generate force on magnets, which can be used to change the direction of magnets by light more efficiently. By creating alloy nanofilms with up to 70% platinum dissolved in cobalt, the team discovered that the unique relativistic quantum mechanical effects of platinum significantly boost the magnetic torque.

While most multiferroics can't operate above room temperature, a team of researchers at Tohoku University demonstrated that terbium oxide Tb₂(MoO₄)₃ works as a multiferroic even at 160°C.

A material that loses its functionality due to heat (from the environment or generated by the device itself) has limited practical applications. This is the major Achilles heel of multiferroics—materials that possess close coupling between magnetism and ferroelectricity. This coupling makes multiferroics an attractive area of research, despite that weakness.

A team of Tohoku University researchers recently investigated a cobalt-manganese-iron alloy thin film that demonstrates a high perpendicular magnetic anisotropy (PMA) - key aspects for fabricating MRAM devices using spintronics.

"This is the first time a cobalt-manganese-iron alloy has strongly shown large PMA," says Professor Shigemi Mizukami from Tohoku University, "We previously discovered this alloy showed a high tunnel magnetoresistance (TMR) effect, but it is rare that an alloy potentially shows both together." For example, Iron-cobalt-boron alloys, which are conventionally used for MRAM, possess both traits, but their PMA is not strong enough.

Researchers at Tohoku University and the University of California, Santa Barbara, have developed new computing hardware that utilizes a Gaussian probabilistic bit made from a stochastic spintronics device. This innovation is expected to provide an energy-efficient platform for generative AI.

As Moore's Law slows down, domain-specific hardware architectures - such as probabilistic computing with naturally stochastic building blocks - are gaining prominence for addressing computationally hard problems. Similar to how quantum computers are suited for problems rooted in quantum mechanics, probabilistic computers are designed to handle inherently probabilistic algorithms. These algorithms have applications in areas like combinatorial optimization and statistical machine learning. 

TDK Corporation has announced the development of a neuromorphic element called a “spin-memristor” that has very low power consumption. By mimicking the energy-efficient operation of the human brain, this element could cut the power consumption of AI applications down to 1/100th of traditional devices. Collaborating with the French research organization CEA (Alternative Energies and Atomic Energy Commission), TDK has shown that its “spin-memristor” can serve as the basic element of a neuromorphic device. 

Going forward, TDK will collaborate with the Center for Innovative Integrated Electronic Systems at Tohoku University on the practical development of the technology.

Researchers from Tohoku University, University of Tokyo, Australian Nuclear Science and Technology Organization, High Energy Accelerator Research Organization and Comprehensive Research Organization for Science and Society have found that the spin current signal changes direction at a certain magnetic temperature and diminishes at lower temperatures.

Spintronics uses electrons’ intrinsic spin, which is vital to the field, to regulate the flow of the spin degree of freedom, that is, spin currents. Scientists are continually exploring new ways to manage spintronics for future uses. Detecting spin currents is quite complicated and necessitates the use of macroscopic voltage measurement, which examines the entire voltage fluctuations across a material. However, a major stumbling block has been a lack of understanding of how the spin current flows or propagates inside the material.

Researchers from Tohoku University and Toho University have demonstrated chirality switching by electric current pulses at room temperature in a thin-film MnAu₂ helimagnetic conductor. The team also succeeded in detecting the chirality at zero magnetic fields by means of simple transverse resistance measurement utilizing the spin Berry phase in a bilayer device composed of MnAu₂ and a spin Hall material Pt. These results may pave the way to helimagnet-based spintronics. 

Helimagnetic structures, in which the magnetic moments are spirally ordered, host an internal degree of freedom called chirality corresponding to the handedness of the helix. The chirality seems quite robust against disturbances and is therefore promising for next-generation magnetic memory. While the chirality control was recently achieved by the magnetic field sweep with the application of an electric current at low temperature in a conducting helimagnet, problems such as low working temperature and cumbersome control and detection methods have to be solved in practical applications.

Researchers from Tohoku University have developed a theoretical model for high-performance spin wave reservoir computing (RC) that utilizes spintronics technology. This achievement could push scientists closer to realizing energy-efficient, nanoscale computing with unparalleled computational power.

Scientists are constantly striving to create neuromorphic devices that mimic the brain's processing capabilities, low power consumption, and its ability to adapt to neural networks. The development of neuromorphic computing is revolutionary, allowing scientists to explore nanoscale realms, GHz speed, with low energy consumption. In recent years, many advances in computational models inspired by the brain have been made. These artificial neural networks have demonstrated extraordinary performances in various tasks. However, current technologies are software-based; their computational speed, size, and energy consumption remain constrained by the properties of conventional electric computers.

One way of processing information in spintronics is to use the magnetic vortices called skyrmions or, alternatively, their still little understood and rarer cousins called 'merons'. Both are collective topological structures formed of numerous individual spins. Merons have to date only been observed in natural antiferromagnets, where they are difficult to both analyze and manipulate.

Working in collaboration with teams at Tohoku University in Japan and the ALBA Synchrotron Light Facility in Spain, researchers of Johannes Gutenberg University Mainz (JGU) have been the first to demonstrate the presence of merons in synthetic antiferromagnets and thus in materials that can be produced using standard deposition techniques.

Researchers at Tohoku University and the University of California, Santa Barbara, have shown a proof-of-concept energy-efficient computer compatible with current AI. It utilizes a stochastic behavior of nanoscale spintronics devices and is particularly suitable for probabilistic computation problems such as inference and sampling.

The team presented the results at the IEEE International Electron Devices Meeting (IEDM 2023) on December 12, 2023.