Evolutionary optimization of all-dielectric magnetic nanoantennas
Nicolas Bonod, Sebastien Bidault, Geoffrey W. Burr, and Mathieu, Mivelle

TL;DR
This paper uses evolutionary algorithms to design dielectric nanoantennas that significantly enhance magnetic light-matter interactions, overcoming previous physical limitations and enabling practical applications.
Contribution
It introduces a novel evolutionary optimization approach to create dielectric nanoantennas with external magnetic hot-spots, surpassing existing designs in magnetic field enhancement.
Findings
Magnetic power density increased by a factor of 5.
Optimized nanostructures have shape robustness.
External magnetic hot-spots enable new applications.
Abstract
Magnetic light and matter interactions are generally too weak to be detected, studied and applied technologically. However, if one can increase the magnetic power density of light by several orders of magnitude, the coupling between magnetic light and matter could become of the same order of magnitude as the coupling with its electric counterpart. For that purpose, photonic nanoantennas have been proposed, and in particular dielectric nanostructures, to engineer strong local magnetic field and therefore increase the probability of magnetic interactions. Unfortunately, dielectric designs suffer from physical limitations that confine the magnetic hot spot in the core of the material itself, preventing experimental and technological implementations. Here, we demonstrate that evolutionary algorithms can overcome such limitations by designing new dielectric photonic nanoantennas, able to…
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