A potential game-changer for the next generation of microelectronics – ScienceDaily

Tiny magnetic vortices could transform memory storage in high-performance computers.

Magnets generate invisible fields that attract certain substances. A common example is fridge magnets. Much more important in our daily life, magnets can also store data in computers. By exploiting the direction of the magnetic field (say, up or down), each microscopic magnetic tape can store one bit of memory as a zero or a one – the computers language.

Scientists at the US Department of Energy’s Argonne National Laboratory want to replace bar magnets with tiny magnetic vortices. These vortices, which are billionths of a meter in size, are called Skyrmions, and they form in certain magnetic materials. They could one day usher in a new generation of microelectronics for memory storage in high-performance computers.

“Bar magnets in computer memory are like shoelaces tied in a single knot; it takes almost no energy to untie them,” said Arthur McCrae, a Northwestern University graduate student in the Department of Materials Science (MSD) at Argonne. Any malfunction of the bar magnet due to some disturbances will affect others.

“By contrast, a shoe is like shoelaces tied in a double knot. No matter how hard you pull the strand, the shoelaces stay tied.” Skyrmions are thus very stable to any perturbation. Another important feature is that scientists can control their behavior by changing the temperature or applying an electric current.

Scientists have a lot to learn about the behavior of the sky under different conditions. To study them, the Argonne-led team developed artificial intelligence (AI) software working with a high-power electron microscope at the Center for Nanomaterials (CNM), a facility used for DOE’s Office of Science in Argonne. A microscope can visualize the sky in samples at extremely low temperatures.

The team’s magnetic material is a mixture of iron, germanium, and tellurium. In structure, this substance resembles a stack of paper with many leaves. A stack of these sheets contains several skyscrapers, and one sheet of paper can be peeled off the top and analyzed in facilities such as CNM.

“CNM electron microscopy coupled with a form of artificial intelligence called machine learning enabled us to visualize Skyrmion sheets and their behavior at different temperatures,” said Yue Li, a postdoctoral appointee at MSD.

“Our most interesting finding was that the sky is arranged in a very ordered pattern at temperatures below 60 degrees Fahrenheit and above,” said Charudatta Phatak, materials scientist and group leader at MSD. ? “But when we cool the sample, the arrangement of the sky changes.” Like bubbles in beer foam, some of the sky got bigger, some smaller, some merged and some disappeared.

At minus 270, the class fell into near complete chaos, but order returned when the temperature returned to minus 60. This shift in order perturbation with changing temperature can be exploited in future microelectronics for memory storage.

“We estimate that the energy efficiency of Sky could be 100 to 1,000 times better than current memory in the high-performance computers used in research,” McCrae said.

Energy efficiency is essential for the next generation of microelectronics. Microelectronics today accounts for nearly 10% of the world’s electricity. This number could double by 2030. More energy-efficient electronics must be found.

“We have a way to cut through it before the sky crush finds its way into any future low-power computer memory,” Fatak said. ? “However, this kind of radical new thinking in microelectronics is key to next-generation devices.”

This research was supported by the Department of Energy’s Office of Basic Energy Sciences. The team’s machine learning software ran on supercomputing resources at the Argonne Leadership Computing Facility, a user facility of the Department of Energy’s Office of Science.

This research appeared in Nano Letters. In addition to Phatak, Li, and McCray, Argonne authors include Amanda K. Petford-Long, Daniel P. Phelan, and Xuedan Ma. Other authors include Rabindra Basnett, Krishna Pandey, and Jane Hu of the University of Arkansas.

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