Optimization of Magnetoplasmonic ε-Near-Zero Nanostructures Using a Genetic Algorithm

Magnetoplasmonic permittivity-near-zero ([Formula: see text]-near-zero) nanostructures hold promise for novel highly integrated (bio)sensing devices. These platforms merge the high-resolution sensing from the magnetoplasmonic approach with the [Formula: see text]-near-zero-based light-to-plasmon coupling (instead of conventional gratings or bulky prism couplers), providing a way for sensing devices with higher miniaturization levels. However, the applications are mostly hindered by tedious and time-consuming numerical analyses, due to the lack of an analytical relation for the phase-matching condition. There is, therefore, a need to develop mechanisms that enable the exploitation of magnetoplasmonic [Formula: see text]-near-zero nanostructures’ capabilities. In this work, we developed a genetic algorithm (GA) for the rapid design (in a few minutes) of magnetoplasmonic nanostructures with optimized TMOKE (transverse magneto-optical Kerr effect) signals and magnetoplasmonic sensing. Importantly, to illustrate the power and simplicity of our approach, we designed a magnetoplasmonic [Formula: see text]-near-zero sensing platform with a sensitivity higher than [Formula: see text] and a figure of merit in the order of [Formula: see text]. These last results, higher than any previous magnetoplasmonic [Formula: see text]-near-zero sensing approach, were obtained by the GA intelligent program in times ranging from 2 to 5 min (using a simple inexpensive dual-core CPU computer).