Researchers unlock potential of 2D magnetic devices for future computing

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Researchers unlock potential of 2D magnetic devices for future computing
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A research team has created an innovative method to control tiny magnetic states within ultrathin, two-dimensional van der Waals magnets -- a process akin to how flipping a light switch controls a bulb.

Imagine a future where computers can learn and make decisions in ways that mimic human thinking, but at a speed and efficiency that are orders of magnitude greater than the current capability of computers.

Tian was corresponding author of a paper, titled"Tunneling current-controlled spin states in few-layer van der Waals magnets," that was published today in, an open access, multidisciplinary journal dedicated to publishing high-quality research in all areas of the biological, health, physical, chemical, Earth, social, mathematical, applied and engineering sciences.

Specifically,"this tunneling current not only can control the switching direction between two stable spin states, but also induces and manipulates switching between metastable spin states, called stochastic switching," says ZhuangEn Fu, a graduate student in Tian's research lab and now a postdoctoral fellow at the University of Maryland.

"In our work, we've developed what you might think of as a probabilistic bit, which can switch between '0' and '1' based on the tunneling current controlled probabilities," Tian says."These bits are based on the unique properties of ultrathin 2D magnets and can be linked together in a way that is similar to neurons in the brain to form a new kind of computer, known as a probabilistic computer.

The study was funded through grants from the U.S. Department of Energy; Wyoming NASA EPSCoR ; the National Science Foundation; and the World Premier International Research Center Initiative and the Ministry of Education, Culture, Sports, Science and Technology, both in Japan.ZhuangEn Fu, Piumi I. Samarawickrama, John Ackerman, Yanglin Zhu, Zhiqiang Mao, Kenji Watanabe, Takashi Taniguchi, Wenyong Wang, Yuri Dahnovsky, Mingzhong Wu, TeYu Chien, Jinke Tang, Allan H. MacDonald, Hua Chen, Jifa Tian.

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