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The rapid rise in AI applications has placed increasingly heavy demands on our energy infrastructure. All the more reason to find energy-saving solutions for AI hardware. One promising idea is the use of so-called spin waves to process information.

A team from the Universities of Münster and Heidelberg led by physicist Prof. Rudolf Bratschitsch (Münster) has now developed a new way to produce waveguides in which the spin waves can propagate particularly far. They have thus created the largest spin waveguide network to date.

Furthermore, the group succeeded in specifically controlling the properties of the spin wave transmitted in the waveguide. For example, they were able to precisely alter the wavelength and reflection of the spin wave at a certain interface. The study was in the journal Nature Materials.

The electron spin is a quantum mechanical quantity that is also described as the intrinsic angular momentum. The alignment of many spins in a material determines its magnetic properties. If an alternating current is applied to a magnetic material with an antenna, thereby generating a changing magnetic field, the spins in the material can generate a spin wave.

Spin waves have already been used to create individual components, such as logic gates that process binary input signals into binary output signals, or multiplexers that select one of various input signals. Up until now, however, the components were not connected to form a larger circuit.

"The fact that larger networks such as those used in electronics have not yet been realized is partly due to the strong attenuation of the spin waves in the waveguides that connect the individual switching elements—especially if they are narrower than a micrometer and therefore on the nanoscale," explains Bratschitsch.

The group used the material with the lowest attenuation currently known: yttrium iron garnet (YIG). The researchers inscribed individual spin-wave waveguides into a 110-nanometer-thin film of this magnetic material using a silicon ion beam and produced a large network with 198 nodes. The new method allows complex structures of high quality to be produced flexibly and reproducibly.