Demetrio Macías

Application of evolution strategies for the solution of an inverse problem in near-field optics

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.

Understanding near/far-field engineering of optical dimer antennas through geometry modification

Numerical investigations based on the boundary element method (BEM) have been carried out to two-dimensional (2-D) silver dimer nano-antennas of various geometries. The near-field and far-field properties are mainly determined by the local geometry at the gap and the global shape of the antenna shafts respectively. A hybrid dimer antenna, which mixes the geometry ingredients of the rod dimer and the bowtie, benefits in both near and far field. Using a microcavity representation, the resonance in dimer nano-antennas is explained in a common and semi-analytical manner. The plasmonic enhancement and the wavelength mismatching in the optical dimer antenna are naturally embodied in this model. The quality factor of the resonance, which can be influenced by the wavelength and the geometry, is discussed intuitively. The understanding presented in this work could guide the future engineering of the optical dimer antenna.

Inverse scattering with far-field intensity data: random surfaces that belong to a well-defined statistical class

We consider the inverse scattering problem of retrieving the surface profile function from far-field angle-resolved intensity data. The problem is approached as a nonlinear constrained optimization problem. The surface, assumed one-dimensional and perfectly conducting, is also assumed to be a realization of a Gaussian random process with a Gaussian correlation function with known standard deviation of heights (δ) and correlation length (a). Starting from rigorously calculated far-field angle-resolved scattered data, we search for the optimum profile using evolutionary strategies. Examples that illustrate the proposed scheme are presented. Aspects of the convergence and lack of uniqueness of the solution are discussed.

Surface Profile Reconstruction from Scattered Intensity Data Using Evolutionary Strategies

We present a study of rough surface inverse scattering problems using evolutionary strategies. The input data consists of far-field angle-resolved scattered intensity data, and the objective is to reconstruct the surface profile function that produced the data. To simplify the problem, the random surface is assumed to be one-dimensional and perfectly conducting. The optimum of the fitness function is searched using the evolutionary strategies (µ, ?) and (µ + ?). On the assumption that some knowledge about the statistical properties of the unknown surface profile is given or can be obtained, the search space is restricted to surfaces that belong to that particular class. In our case, as the original surface, the trial surfaces constitute realizations of a stationary zeromean Gaussian random process with a Gaussian correlation function. We find that, for the conditions and parameters employed, the surface profile can be retrieved with high degree of confidence. Some aspects of the convergence and the lack of uniqueness of the solution are also discussed.

Inverse scattering with a wave-front-matching algorithm.

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.

Hybrid evolution strategy-downhill simplex algorithm for inverse light scattering problems

The rough surface inverse scattering problem is approached with a combination of evolutionary strategies and the simplex method. The surface, assumed one-dimensional and perfectly conducting, is represented using spline curves. Starting from rigorously calculated far-field angle-resolved scattered intensity data, we search for the optimum profile using the evolutionary strategies (µ/ρ+, λ). After a fixed number of iterations, the best surface is finally recovered with the downhill simplex method. Aspects of the convergence and lack of uniqueness of the solution are discussed.

Retrieval of relevant parameters of natural multilayer systems by means of bio-inspired optimization strategies

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.

Inverse Problems in Optical Scattering

One of the main motivations for studying rough surface scattering problems consists in the desire to obtain information about the surface. The information obtained can be of a varied nature. One may be interested, for instance, on the surface profile function, on the optical properties of the surface, or, for random surfaces, on some statistical parameter of the height fluctuations. These are all inverse scattering problems. This chapter contains a review of some aspects of this broad field.

Plasmon spectroscopy: Theoretical and numerical calculations, and optimization techniques

Abstract We present an overview of recent advances in plasmonics, mainly concerning theoretical and numerical tools required for the rigorous determination of the spectral properties of complex-shape nanoparticles exhibiting strong localized surface plasmon resonances (LSPRs). Both quasistatic approaches and full electrodynamic methods are described, providing a thorough comparison of their numerical implementations. Special attention is paid to surface integral equation formulations, giving examples of their performance in complicated nanoparticle shapes of interest for their LSPR spectra. In this regard, complex (single) nanoparticle configurations (nanocrosses and nanorods) yield a hierarchy of multiple-order LSPR s with evidence of a rich symmetric or asymmetric (Fano-like) LSPR line shapes. In addition, means to address the design of complex geometries to retrieve LSPR spectra are commented on, with special interest in biologically inspired algorithms. Thewealth of LSPRbased applications are discussed in two choice examples, single-nanoparticle surface-enhanced Raman scattering (SERS) and optical heating, and multifrequency nanoantennas for fluorescence and nonlinear optics.

Characterization of the iridescence-causing multilayer structure of the Ceroglossus suturalis beetle using bio-inspired optimization strategies

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.