Plasmon hybridization in pyramidal metamaterials: a route towards ultra-broadband absorption

Pyramidal metamaterials are currently developed for ultra-broadband absorbers. They consist of periodic arrays of alternating metal/dielectric layers forming truncated square-based pyramids. The metallic layers of increasing lengths play the role of vertically and, to a less extent, laterally coupled plasmonic resonators. Based on detailed numerical simulations, we demonstrate that plasmon hybridization between such resonators helps in achieving ultra-broadband absorption. The dipolar modes of individual resonators are shown to be prominent in the electromagnetic coupling mechanism. Lateral coupling between adjacent pyramids and vertical coupling between alternating layers are proven to be key parameters for tuning of plasmon hybridization. Following optimization, the operational bandwidth of Au/Ge pyramids, i.e. the bandwidth within which absorption is higher than 90%, extends over a 0.2-5.8 micrometers wavelength range, i.e. from UV-visible to mid-infrared, and total absorption (integrated over the operational bandwidth) amounts to 98.0%. The omni-directional and polarization-independent high-absorption properties of the device are verified. Moreover, we show that the choice of the dielectric layer material (Si versus Ge) is not critical for achieving ultra-broadband characteristics, which confers versatility for both design and fabrication. Realistic fabrication scenarios are briefly discussed. This plasmon hybridization route could be useful in developing photothermal devices, thermal emitters or shielding devices that dissimulate objects from near infrared detectors.Comment: 13 pages, 9 figures, accepted for publication in Optics Expres

Photonic Structures Optimization Using Highly Data-Efficient Deep Learning: Application To Nanofin And Annular Groove Phase Masks

Metasurfaces offer a flexible framework for the manipulation of light properties in the realm of thin film optics. Specifically, the polarization of light can be effectively controlled through the use of thin phase plates. This study aims to introduce a surrogate optimization framework for these devices. The framework is applied to develop two kinds of vortex phase masks (VPMs) tailored for application in astronomical high-contrast imaging. Computational intelligence techniques are exploited to optimize the geometric features of these devices. The large design space and computational limitations necessitate the use of surrogate models like partial least squares Kriging, radial basis functions, or neural networks. However, we demonstrate the inadequacy of these methods in modeling the performance of VPMs. To address the shortcomings of these methods, a data-efficient evolutionary optimization setup using a deep neural network as a highly accurate and efficient surrogate model is proposed. The optimization process in this study employs a robust particle swarm evolutionary optimization scheme, which operates on explicit geometric parameters of the photonic device. Through this approach, optimal designs are developed for two design candidates. In the most complex case, evolutionary optimization enables optimization of the design that would otherwise be impractical (requiring too much simulations). In both cases, the surrogate model improves the reliability and efficiency of the procedure, effectively reducing the required number of simulations by up to 75% compared to conventional optimization techniques

Évaluations par Cartes Conceptuelles à trous et apprentissage par les pairs

Cet article décrit un nouveau dispositif d’évaluation des acquis d’apprentissage basé sur des cartes conceptuelles « à trous » (CCàT) permettant également l’apprentissage par les pairs en grands auditoires durant les tests et une correction automatisée par des formulaires QCM.L’intérêt du dispositif est de garantir une évaluation qualitative (à haut niveau taxonomique) des apprentissages tout en facilitant la conception et la correction de ces évaluations par l’enseignant, même pour de grands groupes d’étudiants (>500) et en favorisant la coopération entre étudiants en amont et pendant les évaluations

Flow photochemistry: a meso-scale reactor for industrial applications

Developing flow photochemistry, especially at meso-scale where significant productivity is required, remains challenging. There is a need for innovative equipments generating highly controlled flow under light irradiation. In this work, a commercial solution, developed by Corning, is presented and studied by LGC and MEPI on an intramolecular (2+2) photo-cycloaddition. Detailed experimental and modelling analysis has been performed to emphasize the flow reactor behaviour and performances, and demonstrate its capability in producing up to 30g.h-1 of the desired molecule. Through this simple model reaction, the G1 photo-reactor is shown to be an efficient meso-scale reactor for industrial photo-applications development and production

Vortex phase masks of topological charge 4 and higher with diamond subwavelength gratings

High contrast imaging at small inner working angles can be achieved using a vector vortex coronagraph in the focal plane of the telescope providing a helical phase ramp with a singularity at its center. The form birefringence of full-diamond subwavelength gratings has proven to be well suited to manufacture such vortex phase masks for coronagraphic applications (Subwavelength Grating Vortex Coronagraph, SGVC). In the past years our group has developed and manufactured SGVCs of topological charge 2 (Annular Groove Phase Mask, AGPM) made of a concentric diamond subwavelength grating. For future applications including ELT-class telescopes in the near- to mid-infrared that will partly resolve nearby stars, it is however useful to increase the topological charge of the vortex. After shortly reviewing our previous attempts at optimizing the grating structure for SGVC of charge 4, we present the first laboratory results obtained with such devices. We then introduce and discuss more realistic simulations compared to prior studies using finite-difference time-domain methods. The quality of the simulation results obtained with the open source software MEEP for an AGPM is shown to be appropriate for developing and assessing the performance of future vortex phase masks. We therefore perform updated simulations for SGVC of charge 4 including various designs with straight and curved grating lines. We conclude with a perspective on the potential of metasurfaces and their applications to design novel vortex coronagraphs based on subwavelength structures.EPI

Bursting test of a silicon carbide micro reactor

This document describes and presents the results of a pneumatic bursting test of a CORNING AFR® G4 industrial silicon carbon reactor performed at INERIS in France. The reactor burst beyond 150 bar, well above its design pressure of 18 bar. The kinetic energy of fragments ejected during the bursting and the pressure effects were measured. The observed effects are below the minimum levels of significant injury for human body

Photonic-structure optimization using highly data-efficient deep learning:Application to nanofin and annular-groove phase masks

Metasurfaces offer a flexible framework for the manipulation of light properties in the realm of thin-film optics. Specifically, the polarization of light can be effectively controlled through the use of thin phase plates. This study aims to introduce a surrogate optimization framework for these devices. The framework is applied to develop two kinds of vortex phase masks (VPMs) tailored for application in astronomical high-contrast imaging. Computational intelligence techniques are exploited to optimize the geometric features of these devices. The large design space and computational limitations necessitate the use of surrogate models like partial least-squares kriging, radial basis functions, or neural networks. However, we demonstrate the inadequacy of these methods in modeling the performance of VPMs. To address the shortcomings of these methods, a data-efficient evolutionary optimization setup using a deep neural network as a highly accurate and efficient surrogate model is proposed. The optimization process in this study employs a robust particle swarm evolutionary optimization scheme, which operates on explicit geometric parameters of the photonic device. Through this approach, optimal designs are developed for two design candidates. In the most complex case, evolutionary optimization enables optimization of the design that would otherwise be impractical (requiring too many simulations). In both cases, the surrogate model improves the reliability and efficiency of the procedure, effectively reducing the required number of simulations by up to 75% compared to conventional optimization techniques

Efficiency enhancement of perovskite solar cells based on opal-like photonic crystals

peer reviewedELSSOL; ARC - Actions de Recherche Concerté