Segmented Bayesian optimization of meta-gratings for sub-wavelength light focusing

Abstract

Using inverse design tools to engineer functional photonic nanostructures has been a subject of great interest over the past several years. We report combining a segmented Bayesian optimization algorithm with the rigorous coupled wave analysis to design meta-gratings for sub-wavelength light focusing. Specifically, the meta-gratings comprise one-dimensional periodic arrays of supercells, each of which consists of dozens of dielectric bars. By optimizing geometry of the structure, we demonstrate two kinds of meta-gratings operating at single and double wavelengths, respectively. Both of them can focus the incoming light into periodic sub-wavelength spots with high energy density. The full width at half-maximum (FWHM) of the focusing spots for the single wavelength (lambda = 633 nm) case can be as small as 0.36 lambda, while FWHMs of the focusing spots at double wavelengths (lambda = 533 nm and 633 nm) are about 0.4 lambda. This proposed approach provides an affordable method to tackle the problem of complex photonic structure design. (C) 2019 Optical Society of America