Tohoku University

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.

Researchers have found that lasers can generate stable patterns of electron spins in a thin layer of semiconductor material, a discovery that may help lead to advanced spin-based memory and computing. The scientists have revealed that lasers could generate complex stable patterns of electron spins called “spin textures” in thin films of semiconductors. These spin textures could help lead to what may be the holy grail of spintronics, a superefficient spin-based transistor.

The new findings are based on how light has momentum, just as a physical object moving through space does, even though light does not have mass. This means that light shining on an object can exert a force. Whereas the linear momentum of light supplies a push in the direction that light is moving, the angular momentum of light applies torque.

Researchers from Tohoku University, University of California Riverside and Massachusetts Institute of Technology (MIT) have highlighted a series of critical achievements in antiferromagnetic spintronics (including their own contributions), revealing an emerging frontier distinguished by the coherent spin dynamics of antiferromagnets. 

Within antiferromagnetic spintronics, scientists have exerted a lot of efforts on the switching and readout of static magnetic order. But coherent spin dynamics, the key to exploring the wave features of spins and integrating spintronics with quantum and neuromorphic technologies, has only received attention very recently. "The coherent spin dynamics of antiferromagnets exhibits a lot more intriguing features than that of ferromagnets," says Jiahao Han, a JSPS Research Fellow working at the Research Institute of Electrical Communication (RIEC), Tohoku University. "By harnessing this unique property, the team has been pursuing breakthroughs that eventually form a new chapter named coherent antiferromagnetic spintronics."

Researchers at Tohoku University, the University of Messina and the University of California, Santa Barbara (UCSB) have developed a scaled-up version of a probabilistic computer (p-computer) with stochastic spintronic devices that is suitable for hard computational problems like combinatorial optimization and machine learning.

The constructed heterogeneous p-computer consisting of stochastic magnetic tunnel junction (sMTJ) based probabilistic bit (p-bit) and field-programmable gate array (FPGA). ©Kerem Camsari, Giovanni Finocchio, and Shunsuke Fukami et al.

A p-computer harnesses naturally stochastic building blocks called probabilistic bits (p-bits). Unlike bits in traditional computers, p-bits oscillate between states. A p-computer can operate at room-temperature and acts as a domain-specific computer for a wide variety of applications in machine learning and artificial intelligence. Just like quantum computers try to solve inherently quantum problems in quantum chemistry, p-computers attempt to tackle probabilistic algorithms, widely used for complicated computational problems in combinatorial optimization and sampling.

Researchers at Tohoku University and the Japan Atomic Energy Agency (JAEA) have reported a new spintronic phenomenon – a persistent rotation of chiral-spin structure.

The researchers studied the response of chiral-spin structure of a non-collinear antiferromagnet Mn3Sn thin film to electron spin injection and found that the chiral-spin structure shows persistent rotation at zero magnetic field. Moreover, their frequency can be tuned by the applied current.

Researchers from Johannes Gutenberg University Mainz (JGU), the University of Konstanz and Tohoku University developed a spin-valve structure based on several ferromagnets - which can detect the efficiency of magnon currents depending on the magnetic configuration of the device.

The researchers say that this is a new "building block" for Magnon Spintronics, and this kind of device could be used to the transmission or blocking of incoming spin information.

Tohoku University published a short video that introduces the spintronics research performed at the University's Center for Science and Innovation in Spintronics:

Researchers at Tohoku University demonstrated a spintronics-based artificial intelligence (AI) device. The researchers developed an artificial neural network using micro-scale magnetic spintronic device.

The researchers say that this spintronic device is capable of memorizing arbitral values between 0 and 1 in an analogue manner unlike the conventional magnetic devices, and thus perform the learning function, which is served by synapses in the brain. This is still an early stage (the researchers call this a proof-of-concept demo) but spintronics has a high potential to enable ultra low-power and fast neural-network devices.

Researchers at Tohoku University managed to find the origin and the mechanism of ferromagnetism in Mn-doped GaAs. This phenomonon has been puzzling researchers for over 20 years, and this new explanation may help to accelerate the development of spintronic devices made from such materials.

Mn-doped GaAs crystals exhibit characteristics and properties of both semiconductor and magnet. It is possible to use an electric field to control the magnetism in such materials - which makes them very appealing candidates for spintronic devices.