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

We report an experimental validation of a machine learning-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 machine learning 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. Since 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 using white light spectroscopy, demonstrating an excellent agreement with the theoretical predictions.