Toward the meta-atom library: Experimental validation of machine learning-based Mie-tronics

Although predicting light scattering by homogeneous spherical particles is a relatively straightforward problem that can be solved analytically, manipulating and studying the scattering behavior of non-spherical particles is a more challenging and time-consuming task, with a plethora of applications ranging from optical manipulation to wavefront engineering, and nonlinear harmonic generation. Recently, physics-driven machine learning (ML) has proven to be instrumental in addressing this challenge. However, most studies on Mie-tronics that leverage ML for optimization and design have been performed and validated through numerical approaches. Here, we report an experimental validation of an ML-based design method that significantly accelerates the development of all-dielectric complex-shaped meta-atoms supporting specified Mie-type resonances at the desired wavelength, circumventing the conventional time-consuming approaches. We used ML to design isolated meta-atoms with specific electric and magnetic responses, verified them within the quasi-normal mode expansion framework, and explored the effects of the substrate and periodic arrangements of such meta-atoms. Finally, we proposed implementing the designed meta-atoms to generate a third harmonic within the vacuum ultraviolet spectrum. Because the implemented method allowed for the swift transition from design to fabrication, the optimized meta-atoms were fabricated, and their corresponding scattering spectra were measured.