Roughness effect on the efficiency of dimer antenna based biosensor

The fabrication process of nanodevices is continually improved. However, most of the nanodevices, such as biosensors present rough surfaces with mean roughness of some nanometers even if the deposition rate of material is more controlled. The effect of roughness on performance of biosensors was fully addressed for plane biosensors and gratings, but rarely addressed for biosensors based on Local Plasmon Resonance. The purpose of this paper is to evaluate numerically the influence of nanometric roughness on the efficiency of a dimer nano-biosensor (two levels of roughness are considered). Therefore, we propose a general numerical method, that can be applied to any other nanometric shape, to take into account the roughness in a three dimensional model. The study focuses on both the far-field, which corresponds to the experimental detected data, and the near-field, responsible for exciting and then detecting biological molecules. The results suggest that the biosensor efficiency is highly sensitive to the surface roughness. The roughness can produce important shifts of the extinction efficiency peak and a decrease of its amplitude resulting from changes in the distribution of near-field and absorbed electric field intensities

Evolutionary optimization of optical antennas

The design of nano-antennas is so far mainly inspired by radio-frequency technology. However, material properties and experimental settings need to be reconsidered at optical frequencies, which entails the need for alternative optimal antenna designs. Here a checkerboard-type, initially random array of gold cubes is subjected to evolutionary optimization. To illustrate the power of the approach we demonstrate that by optimizing the near-field intensity enhancement the evolutionary algorithm finds a new antenna geometry, essentially a split-ring/two-wire antenna hybrid which surpasses by far the performance of a conventional gap antenna by shifting the n=1 split-ring resonance into the optical regime.Comment: Also see Supplementary material, as attached to the main pape

Constraint programming for type inference in flexible model-driven engineering

Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance

Type inference in flexible model-driven engineering using classification algorithms

Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used