Designing photonic nanostructures to obtain particular optical properties is usually based on the systematic variation of very simple models. Unfortunately, such anticipating approach rapidly becomes impracticable due to large numbers of free parameters in the description of more complex geometries. Finally, when multiple properties are to be fulfilled concurrently, it usually fails altogether. In this context, biology-inspired evolutionary techniques, mimicking the natural selection process, can be a promising approach.
In the present study, an evolutionary algorithm for multi-objective optimization was coupled to a numerical framework for the simulation of optical properties : An initial, random “population” of nanoantenna geometries was submitted to a process of evaluation, selection and reproduction, in which the weakest individuals are successively eliminated, keeping only the fittest candidates (the nanoantennas that exhibit the best optical performances regarding the target criteria). After a defined number of evolutionary cycles, the strongest individual was chosen from the final population.
For the demonstration of this procedure, dual-color silicon nanoantennas were automatically designed by the computational scheme. The two resonant wavelengths of these color pixels were designed such, that they could be addressed individually by means of the incident light polarization (see figure). The geometries found by evolutionary optimization were then converted into a lithographic mask, which was used to fabricate silicon nanostructures by electron beam lithography. The optical characterization of the fabricated samples yielded an excellent agreement with the predictions of the optimization algorithm.
This work emphasizes the tremendous potential of evolutionary optimization in photonics and nano-optics. It can be easily adapted to target any other optical property like the directivity of light scattering, for the design of broadband light harvesting antennas and color filters, or for nonlinear optics.
- © CEMES / LAAS / CNRS
Evolutionary Multi-Objective Optimisation of Colour Pixels based on Dielectric Nano-Antennas
P. R. Wiecha, A. Arbouet, C. Girard, A. Lecestre, G. Larrieu, and V. Paillard
Nature Nanotechnology (2016)
DOI : 10.1038/nnano.2016.224
Contacts Researchers :
Arnaud Arbouet - Researcher CNRS - CEMES-CNRS
Guilhem Larrieu - Researcher CNRS - LAAS-NCRS
Vincent Paillard, Professor Université Toulouse Paul Sabatier - CEMES-CNRS
Peter Wiecha, PhD student Université Toulouse Paul Sabatier - CEMES-CNRS
Contact communication INSIS :
insis.communication chez cnrs.fr