December 2024

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.