Tohoku University

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.

Researchers from Tohoku University, Chinese Academy of Sciences (CAS), Guangxi Normal University, Kyushu University and Japan Atomic Energy Agency have reported a significant breakthrough which could revolutionize next-generation electronics by enabling non-volatility, large-scale integration, low power consumption, high speed, and high reliability in spintronic devices.

Spintronic devices, such as magnetic random access memory (MRAM), utilize the magnetization direction of ferromagnetic materials for information storage and rely on spin current, a flow of spin angular momentum, for reading and writing data. Conventional semiconductor electronics have faced limitations in achieving these qualities. However, the emergence of three-terminal spintronic devices, which employ separate current paths for writing and reading information, presents a solution with reduced writing errors and increased writing speed. Nevertheless, the challenge of reducing energy consumption during information writing, specifically magnetization switching, remains a critical concern.

Researchers from Tohoku University in Japan, University of California Riverside and the Massachusetts Institute of Technology detail a decade of research advancements in the emerging field of antiferromagnetic spintronics that holds the promise of moving beyond today’s world of electrons moving through semiconductors.

As computers and other electronic devices become faster and more powerful, they are coming closer to a physical limitation caused by heat generated by the electrons that carry information as they move through semiconductors. “Making heat is a fundamental limit that will prevent the further development of electronic devices. So, we are basically hitting a bottleneck because our computers are way faster than they used to be two decades ago,” said Ran Cheng, an assistant professor of electrical and computer engineering with UCR’s Bourns College of Engineering. Workarounds like cooling systems can go only so far as artificial intelligence, machine learning, video streaming, and other applications demand faster and faster computer processing and memory retrievals.