Researchers at Argonne National Laboratory found a way to better understand and control electron spin behavior in ultrathin magnetic materials, a key step for advancing spintronics. Their work shows how material thickness and magnetic fields shape nanoscale structures like skyrmions, which could be used for ultra-efficient memory and computing.
The findings provide a roadmap for designing faster, smaller, and lower-power electronic devices that could overcome the limits of traditional charge-based electronics. They write:
Data is growing at a staggering pace, pushing charge‑based microelectronics, such as smartphones and laptops, to their physical limits.
Spintronics — technology that uses electron spin rather than charge — avoids the limits of conventional electronics by switching information with very little energy, holding states without power and enabling extremely dense data storage. […]
In groundbreaking new research, scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory reveal how magnetic domains behave inside these 2D van der Waals magnets. This finding provides a roadmap for designing and tuning future spin‑based technologies.
“AI’s growth is pushing the limits of today’s microelectronics. Spintronics could enable faster, smaller, more efficient devices to meet the demand,” said Amanda Petford-Long, materials science researcher emeritus, an Argonne Distinguished Fellow and a co-author of the study published in Advanced Functional Materials. […]
“If engineers can reliably tune skyrmion size and density, they can begin building the kinds of spintronic technologies that have long been imagined. Those with ultra‑dense memory, low‑power processors, and magnetic storage far beyond the capabilities of today’s hard drives,” said Phatak. […]
This work, including use of the CNM, was supported by DOE Office of Basic Energy Sciences. Some computing resources were provided on Swing, a high performance computing cluster operated by the Laboratory Computing Resource Center at Argonne. Garland’s work was supported by the National Science Foundation Graduate Research Fellowship.
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