Based on antiferromagnetic (AFM) materials, the device is the smallest of its kind ever demonstrated and operates with record-low electrical current to write data.
“The rise of big data has enabled the emergence of artificial intelligence (AI) in the cloud and on edge devices and is fundamentally transforming the computing, networking, and data storage industries,” said Pedram哈利利
Khalili co-led the study with Giovanni Finocchio, an associate professor of electrical engineering at the University of Messina. The team also included 马修·格雷森, a professor of electrical and computer engineering at Northwestern Engineering. Jiacheng Shi and Victor Lopez-Dominguez, who are both members of Khalili’s laboratory, served as co-first authors of the paper.
Although AI offers promise to improve many areas of society, including health care systems, transportation, and security, it can only meet its potential if computing can support it.
“There is no existing memory technology that meets all of these demands,” Khalili said. “This has resulted in a so-called ‘memory bottleneck’ that severely limits the performance and energy consumption of AI applications today.”
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Because they are inherently fast and secure and use lower power, AFM materials have been explored in past studies. But previous researchers experienced difficulties controlling the magnetic order within the materials.
Khalili and his team used pillars of antiferromagnetic platinum manganese — a geometry not previously explored. With a diameter of just 800 nanometers, these pillars are 10 times smaller than earlier AFM-based memory devices.
Importantly, the resulting device is compatible with existing semiconductor manufacturing practices, which means that current manufacturing companies could easily adopt the new technology without having to invest in new equipment.
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The study, “Electrical manipulation of the magnetic order in antiferromagnetic PtMn pillars,” was supported by the National Science Foundation (award number ECCS-1853879) and the Air Force Office of Scientific 研究 (award number FA9550-15-1-0377).