Design of plasmonic directional antennas via evolutionary optimization

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.

“pyGDM” - new functionalities and major improvements to the python toolkit for nano-optics full-field simulations

“…We obtain results in agreement with literature [64,65,66], as we find that the emission of the emitter-antenna system points unidirectionally towards the positive X axis (see Fig. 12b).…”

Single plasmon spatial and spectral sorting on a crystalline two-dimensional plasmonic platform

“…The control of the SP wavevector SPs would make possible a better directionality, and a sharper spectral selectivity and rejection from the Bragg mirror. On the other hand, more sophisticated optimization algorithms 41 for coupling and routing at two dimensions might also provide new perspectives in the design of the system.…”

Numerical Optimization Methods for Metasurfaces

“…To properly deal with the extensive (often discrete) parameter space and the existence of several local optima, the majority of inverse design methods of interest for metasurface design are stochastic and include genetic algorithms and evolutionary strategies. [10][11][12][13][14][15][16][17][18] In addition to the methods discussed above, emergent approaches, including artificial neural networks and Bayesian optimization, have the potential to uncover surprising new metasurface designs. We will highlight in this review the key ideas behind these techniques and illustrate their versatility and advantages for the optimization of practical metasurfaces.…”

“…To properly deal with the extensive (often discrete) parameter space and the existence of several local optima, the majority of inverse design methods of interest for metasurface design are stochastic and include genetic algorithms and evolutionary strategies. [ 10–18 ]…”