Genetic-Algorithm-Aided Meta-Atom Multiplication for Improved Absorption and Coloration in Nanophotonics

For a repertoire of nanophotonic systems, including photonic crystals, metasurfaces, and plasmonic structures, unit cell with a single element is conventionally used for the simplicity of design. The extension of the unit cell with multiple meta-atoms drastically enlarges the parameter space and consequently provides potential configurations with improved device performance. Simultaneously, the multiplication does not induce additional complexity for lithography-based fabrications. However, the substantially increased number of parameters makes the design methodology based on physical intuition and parameter sweep impractical. Here, we show that expanding the number of meta-atoms in the unit cell significantly improves the performance of nanophotonic systems by the virtue of a genetic algorithm-based optimizer. Our approach includes physical intuition endowed in the geometry of meta-atoms, providing additional physical understanding of the optimization process. We demonstrate two photonic applications, including prominent enhancement of a broadband absorption and enlargement of the color coverage of plasmonic nanostructures. Not limited to the two proof-of-concept demonstrations, this methodology can be applied to all meta-atom-based nanophotonic systems, including plasmonic near-field enhancement and nonlinear frequency conversion, as well as a simultaneous control of phase and polarization for metasurfaces.