Experimental Demonstration of a Spin-Wave Lens Designed With Machine Learning

In this letter, we present the design and experimental realization of a device that acts like a spin-wave lens i.e., it focuses spin waves to a specified location. The structure of the lens does not resemble any conventional lens design. It is a nonintuitive pattern produced by a machine-learning algorithm. As a spin-wave design tool, we used our custom micromagnetic solver SpinTorch, which has built-in automatic gradient calculation and can perform backpropagation through time for spin-wave propagation. The training itself is performed with the saturation magnetization of a yttrium-iron-garnet (YIG) film as a variable parameter, with the goal to guide spin waves to a predefined location. We verified the operation of the device in the widely used mumax $^{3}$ micromagnetic solver, and by experimental realization. For the experimental implementation, we developed a technique to create effective saturation-magnetization landscapes in YIG by direct focused-ion-beam (FIB) irradiation. This allows us to rapidly transfer the nanoscale design patterns to the YIG medium, without patterning the material by etching. We measured the effective saturation magnetization corresponding to the FIB dose levels in advance and used this mapping to translate the designed scatterer to the required dose levels. Our demonstration serves as a proof of concept for a workflow that can be used to realize more sophisticated spin-wave devices with complex functionality, e.g., spin-wave signal processors, or neuromorphic devices.