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We propose an efficient and versatile optimization scheme, based on the combination of multi-objective genetic algorithms and neural-networks, to reproduce specific colors through the optimization of the geometrical parameters of metal-dielectric diffraction gratings. To illustrate and assess the performance of this approach, we tailor the chromatic response of a structure composed of three adjacent hybrid V-groove diffraction gratings. To be close to the experimental situation, we include the feasibility constraints imposed by the fabrication process. The strength of our approach lies in the possibility to simultaneously optimize different contradictory objectives, avoiding time-consuming electromagnetic calculations.

We propose a metamodel-based optimization technique to tailor the chromatic response of high-contrast-index gratings. The algorithm, which couples a population-based metaheuristic with a neural network, is used to retrieve the optimal geometrical parameters of a grating to reproduce a prescribed color. By means of some examples, we assess the possibilities and limitations of our optimization scheme. The numerical evidence found shows that the metamodel approach offers an alternative to traditional metaheuristic techniques that not only provides the best solution for a given geometry and a material but also significantly improves the computing time required for the optimization process.

In this work, we propose an inversion scheme to tailor the chromatic response of an all-dielectric structure. To this end, we couple, through a previously defined objective functional involving the concept of color difference, a forward solver with an optimization algorithm. The former is based on the differential method, whereas the latter is based on particle swarm optimization. The optimal geometrical parameters of the structure that generates a specific color are obtained through the solution of an approximation problem. We illustrate the performance of our inversion scheme through examples and discuss its limitations and potential applications.

By means of heuristic optimization techniques, we estimate the unknown refractive index of one layer of a periodic natural multilayer system from far-field reflectance data. To take into account the dispersive characteristics of the material, we employ two different strategies. The first is based on the retrieval of Lorentz model-related parameters, to describe the unknown dielectric permittivity within a specific spectral range. The second strategy, based on a wavelength-by-wavelength approach, takes into account the reflectance values for each wavelength at a time. Through some examples, we compare the performance of both strategies when they look for the best estimates and analyze the error involved in each case. The applicability of both approaches to the case of noisy reflectance spectra is also explored.

We investigate the iridescence exhibited by Ceroglossus suturalis beetles, which mostly live endemically in the southern end of South America. Two differently colored specimens have been studied. We observed and characterized the samples by different microscopy techniques, which revealed a multilayer structure within their cuticle. Using measured reflectance spectra as input data, we applied heuristic optimization techniques to estimate the refractive index values of the constituent materials, to be introduced within the theoretical model. The color of the samples was calculated for different incidence angles, showing that multilayer interference is the mechanism responsible for the observed iridescence.

Natural photonic structures exhibit remarkable color effects such as metallic appearance and iridescence. A rigorous study of the electromagnetic response of such complex structures requires to accurately determine some of their relevant optical parameters, such as the refractive indices of the materials involved. In this paper, we apply different heuristic optimization strategies to retrieve the real and imaginary parts of the refractive index of the materials comprising natural multilayer systems. Through some examples, we compare the performances of the inversion methods proposed and show that these kinds of algorithms have a great potential as a tool to investigate natural photonic structures.

In this contribution, we propose a computational tool for the synthesis of metallic nanowires with optimized optical properties, e.g. maximal scattering cross-section at a given wavelength. For this, we employ a rigorous numerical method, based on the solution of surface integral equations, along with a heuristic optimization technique that belongs to the population-based set known as Evolutionary Algorithms. Also, we make use of a general representation scheme to model, in a more realistic manner, the arbitrary geometry of the nanowires. The performance of this approach is evaluated through some examples involving various wavelengths, materials, and optimization strategies. The results of our numerical experiments show that this hybrid technique is a suitable and versatile tool straightforwardly extensible for the design of different configurations of interest in Plasmonics.

We introduce an inversion procedure for the characterization of a nanostructure from near-field intensity data. The method proposed is based on heuristic arguments and makes use of evolution strategies for the solution of the inverse problem as a nonlinear constrained-optimization problem. By means of some examples we illustrate the performance of our inversion method. We also discuss its possibilities and potential applications.

We propose and study a numerical procedure for the reconstruction of surface profiles from far-field scattering data. The algorithm, based on wave-front-matching principles, is used to reconstruct one-dimensional surface profiles from amplitude scattering data calculated by using rigorous techniques. The study is complemented by the development of a sampling strategy and considerations of the tolerance of the algorithm to noise in the data.