An artificial intelligence (AI) program that works with a high-power electron microscope has been developed to study skyrmions by scientists at the Argonne National Laboratory. The microscope can image skyrmions in samples at extremely low temperatures.
Skyrmions are magnetic vortices that form in certain magnetic materials and can be as small as billionths of a meter. They could lead to a new generation of high-performance computer memory storage microelectronics.
Magnets produce intangible fields that attract specific materials. Refrigerator magnets are a common example.
Far more importantly in our daily lives, magnets can also store information in computers. Utilizing the direction of the magnetic field (for example, up or down), microscopic bar magnets can store one bit of memory as a zero or a one — the computer language.
Skyrmion Memory Knots
Magnetic fields created by skyrmions in two-dimensional sheet of material composed of iron, germanium and tellurium.
Credit: Argonne National Laboratory
Argonne researchers hope to replace bar magnets with skyrmion vortices.
“The bar magnets in computer memory are like shoelaces tied with a single knot; it takes almost no energy to undo them. By contrast, skyrmions are like shoelaces tied with a double knot. No matter how hard you pull on a strand, the shoelaces remain tied,”
said Arthur McCray, a Northwestern University graduate student working in Argonne’s Materials Science Division.
Furthermore, any bar magnets that fail due to disruption will affect the others. However, skyrmions are extremely resistant to disruption. Another noteworthy feature is that scientists can manipulate their behavior by varying the temperature or applying an electric current.
Magnetic Sheet Stacks
Scientists still have a lot to learn about skyrmion behaviour under various conditions. Skyrmions in samples at very low temperatures act differently, for example.
The magnetic material used by the team is a combination of tellurium, germanium, and iron. This material is structured similarly to a stack of paper with many sheets.
A stack of such sheets contains many skyrmions, and a single sheet can be peeled from the top and analyzed at facilities such as the Argonne National Laboratory’s Center for Nanoscale Materials (CNM), a DOE Office of Science user facility.
“The CNM electron microscope coupled with a form of AI called machine learning enabled us to visualize skyrmion sheets and their behavior at different temperatures,”
said Yue Li, a postdoctoral appointee in the Materials Science Division.
Order-disorder Transition
The most intriguing discovery was that at minus 60 degrees Fahrenheit and above, the skyrmions are arranged in a highly ordered pattern.
“But as we cool the sample the skyrmion arrangement changes,”
said materials scientist Charudatta Phatak. Some skyrmions grew larger, some smaller, some merged, and some vanished, much like bubbles in beer foam.
The layer reached nearly complete disorder at minus 270, but order was restored when the temperature came back to minus 60. This order-disorder transition with temperature change could be used for memory storage in future microelectronics.
Higher Energy Efficiency
According to McCray, the energy efficiency of skyrmions could be 100 to 1,000 times better than current memory in high-performance computers used in research. For the next generation of microelectronics, energy efficiency is crucial.
Microelectronics already consume approximately 10 percent of the world’s electricity. And by 2030, this number could double. Electronics that use less energy must be developed.
“We have a way to go before skyrmions find their way into any future computer memory with low power,”
Phatak said.
Be that as it may, it is just this sort of radical new approach to microelectronics that is critical for next-generation devices.
Reference:
Arthur R. C. McCray, Yue Li, Rabindra Basnet, Krishna Pandey, Jin Hu, Daniel P. Phelan, Xuedan Ma, Amanda K. Petford-Long, and Charudatta Phatak. Thermal Hysteresis and Ordering Behavior of Magnetic Skyrmion Lattices. Nano Letters 2022 22 (19), 7804-7810 DOI: 10.1021/acs.nanolett.2c02275