Experimental Demonstration of a Spin-Wave Lens Designed with Machine Learning
Martina Kiechle, Levente Maucha, Valentin Ahrens, Carsten Dubs,, Wolfgang Porod, Gyorgy Csaba, Markus Becherer, and Adam Papp

TL;DR
This paper demonstrates a novel spin-wave lens designed via machine learning, combining simulation and experimental techniques to focus spin waves using a nonintuitive pattern created by an AI-driven design process.
Contribution
It introduces a machine learning-based design method for spin-wave devices and experimentally realizes a lens with a custom magnetization landscape created by focused-ion-beam irradiation.
Findings
Successful experimental demonstration of a spin-wave lens
Validation of the design with micromagnetic simulations
Potential for complex spin-wave device fabrication
Abstract
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" that 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 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 mumax3 micromagnetic solver, and by experimental realization. For the experimental implementation, we developed a technique to create effective saturation-magnetization landscapes…
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