Design of plasmonic directional antennas via evolutionary optimization
arXiv:1906.11822 · doi:10.1364/OE.27.029069
Abstract
We demonstrate inverse design of plasmonic nanoantennas for directional light scattering. Our method is based on a combination of full-field electrodynamical simulations via the Green dyadic method and evolutionary optimization (EO). Without any initial bias, we find that the geometries reproducibly found by EO, work on the same principles as radio-frequency antennas. We demonstrate the versatility of our approach by designing various directional optical antennas for different scattering problems. EO based nanoantenna design has tremendous potential for a multitude of applications like nano-scale information routing and processing or single-molecule spectroscopy. Furthermore, EO can help to derive general design rules and to identify inherent physical limitations for photonic nanoparticles and metasurfaces.
arXiv comment13 pages, 9 figures
References in corpus (10)
- Outlook for inverse design in nanophotonics
- Evolutionary optimization of optical antennas
- Optical Yagi-Uda nanoantennas
- Evolutionary Multi-Objective Optimisation of Colour Pixels based on Dielectric Nano-Antennas
- Design parameters for a nano-optical Yagi-Uda antenna
- Plasmonic Purcell factor and coupling efficiency to surface plasmons. Implications for addressing and controlling optical nanosources
- A Global Optimisation Toolbox for Massively Parallel Engineering Optimisation
- Resonances On-Demand for Plasmonic Nano-Particles
- pyGDM -- A python toolkit for full-field electro-dynamical simulations and evolutionary optimization of nanostructures
- Designing Thermoplasmonic Properties of Metallic Metasurfaces
Cited by in corpus (11)
- Deep learning in nano-photonics: inverse design and beyond
- A newcomer's guide to deep learning for inverse design in nano-photonics
- pyGDM -- new functionalities and major improvements to the python toolkit for nano-optics full-field simulations
- Inverse design with flexible design targets via deep learning: Tailoring of electric and magnetic multipole scattering from nano-spheres
- Illustrated tutorial on global optimization in nanophotonics
- Dissipative modes, Purcell factors and directional beta factors in gold bowtie nanoantenna structures
- Control of light emission of quantum emitters coupled to silicon nanoantenna using cylindrical vector beams
- Inverse design of Mie resonators with minimal backscattering
- Directional silicon nano-antennas for quantum emitter control designed by evolutionary optimization
- Magnetic and electric Purcell factor control through geometry optimization of high index dielectric nanostructures
- Gradient-based optimization of scatterer arrangements based on the T-matrix method