Researchers from Helmholtz-Zentrum Dresden-Rossendorf (HZDR), TU Chemnitz, TU Dresden and Forschungszentrum Jülich have been the first to demonstrate that not only individual bits, but entire sequences of bits can be stored in cylindrical domains: tiny cylindrical areas measuring about 100 nanometers. As the team reports in the journal Advanced Electronic Materials (DOI: https://doi.org/10.1002/aelm.202400251), these results could pave the way for new types of data storage and sensors, including magnetic variants of neural networks.
“A cylindrical domain, which we physicists also call a bubble domain, is a tiny cylindrical area in a thin magnetic layer. Its spins, the intrinsic angular momentum of the electrons that generates the magnetic moment in the material, point in a specific direction. This creates a different magnetization from the rest of the environment. Imagine a small cylinder-shaped magnetic bubble floating in a sea of opposing magnetization,” says Professor Olav Hellwig from the Institute of Ion Beam Physics and Materials Research at the HZDR, describing the topic of his research. He and his team are convinced that such magnetic structures have great potential for spintronic applications.
At the edges of this cylindrical domain, domain walls form, marginal areas in which the direction of magnetization changes. In the magnetic storage technology that Hellwig’s team is trying to develop, it will be crucial to precisely control the spin structure in the domain wall, because its clockwise or counterclockwise direction can be used directly to encode bits. The researchers are also focusing on another aspect: “Our current hard drives, with their track widths of 30 to 40 nanometers and bit lengths of 15 to 20 nanometers, can hold about a terabyte on an area the size of a postage stamp. We are working to overcome this limitation in data density by extending storage to the third dimension,” Hellwig explains.
The solution: 3D metamaterials
Magnetic multilayer structures are an interesting method to control the internal spin structure of domain walls, because the magnetic energies involved can be tuned by combining different materials and layer thicknesses. Hellwig’s team used blocks of alternating layers of cobalt and platinum, separated by layers of ruthenium, and deposited them on silicon wafers. The resulting metamaterial is a synthetic antiferromagnet. Its special feature is a vertical magnetization structure in which adjacent layer blocks have opposite magnetization directions, resulting in an overall neutral net magnetization.
“This is where the concept of ‘racetrack’ memory comes in. The system resembles a racetrack on which the bits are arranged like a pearl necklace. The ingenuity of our system lies in the fact that we can specifically control the thickness of the layers and thus their magnetic properties. This allows us to tailor the magnetic behavior of the synthetic antiferromagnet to enable the storage of not only individual bits, but entire bit sequences, in the form of a depth-dependent magnetization direction of the domain walls,” explains Hellwig. This opens up the prospect of transporting such multi-bit cylindrical domains along these magnetic data highways in a controlled, fast and energy-efficient manner.
Other applications of magnetoelectronics are also possible. For example, they can be used in magnetoresistive sensors or in spintronic components. In addition, these complex magnetic nano-objects show great potential for magnetic implementations in neural networks that could process data in a similar way to the human brain.
Researchers from Helmholtz-Zentrum Dresden-Rossendorf (HZDR), TU Chemnitz, TU Dresden and Forschungszentrum Jülich have been the first to demonstrate that not only individual bits, but entire sequences of bits can be stored in cylindrical domains: tiny cylindrical areas measuring around 100 nanometers. As the team reports in the journal Advanced Electronic Materials (DOI: 10.1002/aelm.202400251), these results could pave the way for new types of data storage and sensors, including magnetic variants of neural networks.
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