Recent Spintronic News - Page 3

Researchers from the National University of Singapore (NUS), working with teams from University of California, Kyoto University and others, have reported a breakthrough in the development of next-generation graphene-based quantum materials, opening new horizons for advancements in quantum electronics.

The innovation involves a novel type of graphene nanoribbon (GNR) named Janus GNR (JGNR). The material has a unique zigzag edge, with a special ferromagnetic edge state located on one of the edges. This unique design enables the realization of one-dimensional ferromagnetic spin chain, which could have important applications in quantum electronics and quantum computing.

Researchers at the Research Center for Materials Nanoarchitectonics (MANA) recently proposed a method to create ferroelectric-ferromagnetic materials, opening doors to advancing spintronics and memory devices.

In 1831, Michael Faraday discovered the fundamental connection between electricity and magnetism, demonstrating that a changing magnetic field induces electric current in a conductor. In a recent study, MANA researchers proposed a method for designing ferroelectric-ferromagnetic (FE-FM) materials, which exhibit both ferroelectric and ferromagnetic properties, enabling the manipulation of magnetic properties using electric fields and vice versa. Such materials are highly promising for spintronics and memory devices. The advantage of FE-FM materials, extremely rare in nature, is their ability to achieve the cross-control by relatively low electric and magnetic fields. The study, led by Principal Researcher Igor Solovyev from MANA, NIMS, included contributions from Dr. Ryota Ono from MANA, NIMS, and Dr. Sergey Nikolaev from the University of Osaka, Japan.

Here are some recent and popular spintronics industry and research news that you may find of interest:

• Researchers report "somersaulting spin qubits"
• Researchers develop non-thermal method to alter magnetization using XUV radiation
• Researchers tackle key obstacles to bringing 2D magnetic materials into practical use
• New EU-funded project applies spintronics to the field of artificial intelligence
• Intel unveils a highly promising MESO spintronics device architecture
• Researchers report a new type of magnetism called altermagnetism
• Researchers develop spin-selective memtransistors with magnetized graphene
• Researchers report spintronics-based probabilistic computers compatible with current AI
• Neuranics secures $2.3 million investment
• Researchers develop atomic-scale spin-optical laser
• Researchers use perovskite materials and light to control electron spins
• New $7.5M project to leverage atomic-scale defects for next-generation processing
• Singapore’s National Research Foundation gives 'substantial funding' for spintronics devices

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 from the University of Missouri and Oak Ridge National Laboratory have discovered a new type of quasiparticle found in all magnetic materials, no matter their strength or temperature. These new properties shake up what researchers previously knew about magnetism, showing it’s not as static as once believed.

“We’ve all seen the bubbles that form in sparkling water or other carbonated drink products,” said Ullrich, Curators’ Distinguished Professor of Physics and Astronomy. “The quasiparticles are like those bubbles, and we found they can freely move around at remarkably fast speeds”. This discovery could help the development of a new generation of electronics that are faster, smarter and more energy efficient. But first, scientists need to determine how this finding could work into those processes.

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. 

Researchers from Kyoto University, Chiba University, The University of Tokyo, Osaka University and Tokai University have found that the direction of spins inside a special type of magnet can be changed rapidly - flipping about every trillionth of a second - without increasing the temperature. They achieved this by applying a strong magnetic field with an oscillation frequency in the terahertz range.

The background for this work, according to the scientists, is the ever-increasing amount of information handled by computers and communication devices, that is driving development of technologies using the terahertz band - around 1012 Hz, a frequency range beyond the conventional gigahertz range of 109 Hz - considered important for the post-5G era. Additionally, memory technologies based on spintronics are expected to use less power to store more information, with antiferromagnets attracting attention because their collective spin-motion mode frequency reaches the terahertz range, making it possible to control spins using terahertz waves. However, conventional spin excitation using electric-field pulses is accompanied by heating or carrier excitation effects that subside relatively slowly, making it difficult to achieve fast spin control. The team has now demonstrated non-thermal spin switching in a canted antiferromagnet by dynamically modifying the magnetic energy landscape using a strong multicycle terahertz magnetic near-field.

Researchers from Korea have designed a new MRAM structure, based on graphene, that offers higher efficiency (and lower heat generation) compared to existing MRAM solutions. The design of the MRAM device is based on a graphene layer sandwiched between a magnetic insulator (yttrium iron garnet) and a ferroelectric material (PVDF-TrFE). Upon application of a voltage pulse, the current flow through the graphene is altered, enabling the storage of binary data based on this current direction.

The recent study demonstrates non-volatile control of spin-charge conversion at room temperature in graphene-based heterostructures through Fermi level tuning. The team used a polymeric ferroelectric film to induce non-volatile charging in graphene. To demonstrate the switching of spin-to-charge conversion, the scientists performed ferromagnetic resonance and inverse Edelstein effect experiments. 

A team of researchers from CIC nanoGUNE, IKERBASQUE, IMEC and CNRS have reported a spintronic device that leverages proximity effects alone, specifically a 2D graphene-based spin valve. The functioning of this valve relies only on the proximity to the van der Waals magnet Cr₂Ge₂Te₆. Spin precession measurements showed that the graphene acquires both spin–orbit coupling and magnetic exchange coupling when interfaced with the Cr₂Ge₂Te₆. This leads to spin generation by both electrical spin injection and the spin Hall effect, while retaining spin transport. The simultaneous presence of spin–orbit coupling and magnetic exchange coupling also leads to a sizeable anomalous Hall effect.

The primary objective of this recent study was to tackle a long-standing research challenge, namely that of realizing the first-ever seamless 2D spintronic device. The spin valve they developed could enable the manipulation and transport of spin entirely in the 2D plane.

Researchers from the Tokyo University of Science have proposed a novel magnetic RAM-based architecture that leverages spintronics to realize smaller, more efficient AI-capable circuits.

(a) Structure of the proposed neural network, which uses three-valued gradients during backpropagation (training) rather than real numbers, thus minimizing computational complexity. (b) A novel magnetic RAM cell leveraging spintronics for implementing the proposed technique in a computing-in-memory architecture.   

Artificial intelligence (AI) and the Internet of Things (IoT) are two technological fields that have been developing at an increasingly fast pace over the past decade. By excelling at tasks such as data analysis, image recognition, and natural language processing, AI systems have become undeniably powerful tools in both academic and industry settings. Meanwhile, miniaturization and advances in electronics have made it possible to massively reduce the size of functional devices capable of connecting to the Internet. Engineers and researchers alike foresee a world where IoT devices are ubiquitous, comprising the foundation of a highly interconnected world. However, bringing AI capabilities to IoT edge devices presents a significant challenge. Artificial neural networks (ANNs)—one of the most important AI technologies—require substantial computational resources. Meanwhile, IoT edge devices are inherently small, with limited power, processing speed, and circuit space. Developing ANNs that can efficiently learn, deploy, and operate on edge devices is a major hurdle.