Demetrio Macias, Alexandre Vial, Elena Ionescu
Past PhD students : Anne-Sophie Grimault, Montacer Dridi, Huan Wang, Loic Le Cunff
The research activity of this group is devoted to the modelling of the interaction between light and metallic nano-structures through the employment of different numerical or semi-analytic approaches such as the Finite-Differences Time-Domain Method (FDTD), the Boundary-Elements Method (BEM) or Mie's Theory, to name but a few examples, and to the resolution of inverse problems by means of stochastic optimization techniques like Simulated Annealing or Evolutionary Algorithms, for instance.
The strong collaboration between the different research groups at the LNIO, whose activities are mainly experimental, and the modelling axis conferes to the latter a noteworthy transverse character that, broadly speaking, serves to foresee and characterize the behavior of a particular experiment, through the employment of the numerical simulation and optimization techniques mentioned in the previous paragraph.
A representative example that illustrates one part our research activities is the study of the problems associated with the description of the dielectric constant of metals, frequently described by means of Drude-Lorentz' model [5,6,8,11]. However, the number of terms required to achieve an accurate representation of the permittivity, within a large spectral interval, makes of this approach prohibitive in terms of computing time and memory. To circumvent this limitation, we have focused our attention on the Critical Points Method (CPM) , that provides, with a limited number of terms, a better and less computationally expensive description of the permitivity of different metals such as, for instance, gold, silver, aluminum or copper [9-10]. This has opened the way to carry on spectroscopic studies in which not only it is possible to consider large spectral intervals, but also it is possible to have different kinds of metals in the system studied. Several works employing this approach, have been conducted in collaboration with teams 1 and 2 [12-15].
We have also investigated two fundamental facets of the inverse problem. In the first of them we were interested on the retrieval of some unknown geometrical or material parameters of a nano-structure, e.g. its size or dielectric constant, and also on the illumination and detection conditions, for example, the polarization, the angle of incidence or the angles of detection [1-4]. For this, although the choice is not restrictive and we could have used any other direct method, we employed the FDTD together with a Stochastic Optimization method known as Evolutionary Algorithms. This is a population-based technique that mimics the process of variation and selection that take place in nature through the "genetic operators" of recombination, mutation and selection. Recently, we have used our inversion method to explore the other aspect of the inverse problem that consists in the optimization of certain characteristics of scattered near-field as the visibility of a plasmons interference pattern or the near-field scattered intensity, through the controlled variation of the geometry and illumination conditions of the sample.