arXiv subjects

Antoine Moreau

Publications and source records attributed to Antoine Moreau.

At least 19 recordsLinked to original sources

arXiv2026

Engineering correlated disorder for tailored light scattering

Correlated disorder is known to shape light scattering in ways uncorrelated disorder cannot, from hyperuniform transparency to the structural colors of naturally occurring structures. What has been missing in photonics is a direct link between the disorder and the scattering pattern it produces. Here we show that adding correlated noise to a periodic array splits the scattering pattern into three distinct components: diffraction peaks, a diffuse background, and correlation halos. Often mistaken for broadened diffraction peaks, these halos are in fact independent features: their positions are set by the correlation range, meaning that they can appear between Bragg peaks, and - crucially - they persist far beyond the regime where the diffraction peaks vanish. Shaping the disorder itself offers further control: tuning the noise distribution suppresses selected diffraction peaks, while tuning the correlation statistics moves the halos away from the Bragg positions. This approach reproduces the scattering signatures of natural photonic structures, such as Morpho butterfly wings, and reveals multiple pathways from order to disorder, each with distinct optical properties. It also offers a practical route to inverse design - finding the disorder that produces a desired scattering pattern. This establishes scattering as a designable quantity, expanding the toolkit for metasurfaces and structural colors.

physics.optics↗

arXiv2024

Optical excitation of bulk plasmons in n-doped InAsSb thin films : investigating the second viscosity in electron gas

We demonstrate that including the second viscosity of an electron gas in the hydrodynamic model allows for highly accurate modeling of the optical response of heavily doped semiconductors. In our setup, which improves resonance visibility compared to previous approaches, plasmon resonances become more distinct, allowing for a detailed analysis of the underlying physics. With advanced fitting techniques based on a physics-informed cost function and a tailored optimization algorithm, we obtain a close agreement between simulations and experimental data across different sample thicknesses. This enhanced resonance visibility, combined with our integrated approach, shows that key parameters such as doping level and effective electron mass, as well as the second viscosity of the electron gas, can be retrieved from a single optical measurement. The spatial dispersion taken into account in the hydrodynamic framework is essential for accurately describing the optical response of plasmonic materials in this frequency range and is likely to become a standard modeling approach.

physics.optics↗

arXiv2024

Illustrated tutorial on global optimization in nanophotonics

Numerical optimization for the inverse design of photonic structures is a tool which is providing increasingly convincing results -- even though the wave nature of problems in photonics makes them particularly complex. In the meantime, the field of global optimization is rapidly evolving but is prone to reproducibility problems, making it harder to identify the right algorithms to use. This paper is thought as a tutorial on global optimization for photonic problems. We provide a general background on global optimization algorithms and a rigorous methodology for a physicist interested in using these tools -- especially in the context of inverse design. We suggest algorithms and provide explanations for their efficiency. We provide codes and examples as an illustration than can be run online, integrating quick simulation code and Nevergrad, a state-of-the-art benchmarking library. Finally, we show how physical intuition can be used to discuss optimization results and to determine whether the solutions are satisfactory or not.

physics.optics↗

arXiv2023

A newcomer's guide to deep learning for inverse design in nano-photonics

Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as light concentration, routing, and filtering. Designing these devices is a challenging task. Traditionally, solving this problem has relied on computationally expensive, iterative methods. In recent years, deep learning techniques have emerged as promising tools for tackling the inverse design of nanophotonic devices. While several review articles have provided an overview of the progress in this rapidly evolving field, there is a need for a comprehensive tutorial that specifically targets newcomers without prior experience in deep learning. Our goal is to address this gap and provide practical guidance for applying deep learning to individual scientific problems. We introduce the fundamental concepts of deep learning and critically discuss the potential benefits it offers for various inverse design problems in nanophotonics. We present a suggested workflow and detailed, practical design guidelines to help newcomers navigate the challenges they may encounter. By following our guide, newcomers can avoid frustrating roadblocks commonly experienced when venturing into deep learning for the first time. In a second part, we explore different iterative and direct deep learning-based techniques for inverse design, and evaluate their respective advantages and limitations. To enhance understanding and facilitate implementation, we supplement the manuscript with detailed Python notebook examples, illustrating each step of the discussed processes. While our tutorial primarily focuses on researchers in (nano-)photonics, it is also relevant for those working with deep learning in other research domains. We aim at providing a solid starting point to empower researchers to leverage the potential of deep learning in their scientific pursuits.

physics.optics↗

arXiv2023

PyMoosh : a comprehensive numerical toolkit for computing the optical properties of multilayered structures

We present PyMoosh, a Python-based simulation library designed to provide a comprehensive set of numerical tools allowing to compute essentially all optical characteristics of multilayered structures, ranging from reflectance and transmittance to guided modes and photovoltaic efficiency. PyMoosh is designed not just for research purposes, but also for use-cases in education. To this end, we have invested significant effort in ensuring user-friendliness and simplicity of the interface. PyMoosh has been developed in line with the principles of Open Science and taking into account the fact that multilayered structures are increasingly being used as a testing ground for optimization and deep learning approaches. We provide in this paper the theoretical basis at the core of PyMoosh, an overview of its capabilities, as well as a comparison between the different numerical methods implemented in terms of speed and stability. We are convinced such a versatile tool will be useful for the community in many ways.

physics.comp-ph↗

arXiv2021

Generalized electromagnetic theorems for non-local plasmonics

The ultraconfined light of plasmonic modes put their effective wavelength close to the mean free path of electrons inside the metal electron gas. The Drude model, which can not take the repulsive interactions of electrons into account, then clearly begins to show its limits. In an intermediate length scale where a full quantum treatment is computationally prohibitive, the semiclassical hydrodynamic model, instrinsically non-local, has proven successful. Here we generalize the expression for the absorption volume density and the reciprocity theorem in the framework of this hydrodynamic model. We validate numerically these generalized theorems and show that using classical expressions instead leads to large discrepancies.

physics.optics↗

arXiv2021

Analysis and fabrication of a photonic crystal based anti-reflective coating for photovoltaics generated by evolutionary optimization

We optimize multilayered anti-reflective coatings for photovoltaic devices, using modern evolutionary algorithms. We apply a rigorous methodology to show that a given structure, which is particularly regular, emerge spontaneously in a very systematical way for a very broad range of conditions. The very regularity of the structure allows for a thorough physical analysis of how the designs operate. This allows to understand that the central part is a photonic crystal utilized as a buffer for light, and that the external layers have the purpose of reducing the impedance mismatch between the outer media and the Bloch mode supported by the photonic crystal. This shows how optimization can suggest new design rules and be considered as a source of inspiration. Finally, we fabricate these structures with easily deployable techniques.

physics.optics↗

arXiv2021

Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking

Existing studies in black-box optimization for machine learning suffer from low generalizability, caused by a typically selective choice of problem instances used for training and testing different optimization algorithms. Among other issues, this practice promotes overfitting and poor-performing user guidelines. To address this shortcoming, we propose in this work a benchmark suite, OptimSuite, which covers a broad range of black-box optimization problems, ranging from academic benchmarks to real-world applications, from discrete over numerical to mixed-integer problems, from small to very large-scale problems, from noisy over dynamic to static problems, etc. We demonstrate the advantages of such a broad collection by deriving from it Automated Black Box Optimizer (ABBO), a general-purpose algorithm selection wizard. Using three different types of algorithm selection techniques, ABBO achieves competitive performance on all benchmark suites. It significantly outperforms previous state of the art on some of them, including YABBOB and LSGO. ABBO relies on many high-quality base components. Its excellent performance is obtained without any task-specific parametrization. The OptimSuite benchmark collection, the ABBO wizard and its base solvers have all been merged into the open-source Nevergrad platform, where they are available for reproducible research.

cs.LG↗

arXiv2020

Versatile Black-Box Optimization

Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization. It is also a good tool for making evolutionary algorithms fast, robust and versatile. We present Shiwa, an algorithm good at both discrete and continuous, noisy and noise-free, sequential and parallel, black-box optimization. Our algorithm is experimentally compared to competitors on YABBOB, a BBOB comparable testbed, and on some variants of it, and then validated on several real world testbeds.

cs.AI↗

arXiv2019

Ultra thin anti-reflective coatings designed using Differential Evolution

We use a state-of-the-art optimization algorithm combined with a careful methodology to find optimal anti-reflective coatings. Our results show that ultra thin structures (less than $300 \,nm$ thick) outperform much thicker gradual patterns as well as traditional interferential anti-reflective coatings. These optimal designs actually combine a gradual increase of the refractive index with patterns meant to leverage interferential effects. Contrarily to gradual patterns, they do not require extremely low refractive index materials, so that they can actually be fabricated. Using a cheap and easily deployable vapor deposition technique, we fabricated a 4-layer anti-reflective coating, which proved very efficient over the whole visible spectrum despite a total thickness of only 200 nm.

physics.optics↗

arXiv2019

The energy point of view in plasmonics

The group velocity of a plasmonic guided mode can be written as the ratio of the flux of the Poynting to the integral of the energy density along the profile of the mode. This theorem, linking the way energy propagates in metals to the properties of guided modes and Bloch modes in a multilayer, provides a unique physical insight in plasmonics. It allows to better understand the link between the negative permittivity of metals and the wide diversity of exotic phenomenon that occur in plasmonics -- like the slowing down of guided modes, the high wavevector and the negative refraction.

physics.optics↗

arXiv2019

Influence of spatial dispersion on surface plasmons, nanoparticles and grating couplers

Recent experiments have shown that spatial dispersion may have a conspicuous impact on the response of plasmonic structures. This suggests that in some cases the Drude model should be replaced by more advanced descriptions that take spatial dispersion into account, like the hydrodynamic model. Here we show that nonlocality in the metallic response affects surface plasmons propagating at the interface between a metal and a dielectric with high permittivity. As a direct consequence, any nanoparticle with a radius larger than 20 nm can be expected to be sensitive to spatial dispersion whatever its size. The same behavior is expected for a simple metallic grating allowing the excitation of surface plasmons, just as in Wood's famous experiments. Importantly, our work suggests that for any plasmonic structure in a high permittivity dielectric, nonlocality should be taken into account.

physics.optics↗

arXiv2018

Evolutionary algorithms converge towards evolved biological photonic structures

Nature features a plethora of extraordinary photonic architectures that have been optimized through natural evolution. While numerical optimization is increasingly and successfully used in photonics, it has yet to replicate any of these complex naturally occurring structures. Using evolutionary algorithms directly inspired by natural evolution, we have retrieved emblematic natural photonic structures, indicating how such regular structures might have spontaneously emerged in nature and to which precise optical or fabrication constraints they respond. Comparisons between algorithms show that recombination between individuals inspired by sexual reproduction confers a clear advantage in this context of modular problems and suggest further ways to improve the algorithms. Such an in silico evolution can also suggest original and elegant solutions to practical problems, as illustrated by the design of counter-intuitive anti-reflective coating for solar cells.

physics.optics↗

arXiv2018

Plasmonic enhancement of spatial dispersion effects in prism coupler experiments

Recent experiments with film-coupled nanoparticles suggest that the impact of spatial dispersion is enhanced in plasmonic structures where high wavevector guided modes are excited. More advanced descriptions of the optical response of metals than Drude's are thus probably necessary in plasmonics. We show that even in classical prism coupler experiments, the plasmonic enhancement of spatial dispersion can be leveraged to make such experiments two orders of magnitude more sensitive. The realistic multilayered structures involved rely on layers that are thick enough to rule our any other phenomenon as the spill-out. Optical evanescent excitation of plasmonic waveguides using prism couplers thus constitutes an ideal platform to study spatial dispersion.

physics.optics↗

arXiv2017

Fresnel coefficients and Fabry-Perot formula for spatially dispersive metallic layers

The repulsion between free electrons inside a metal makes its optical response spatially dispersive, so that it is not described by Drude's model but by a hydrodynamic model. We give here fully analytic results for a metallic slab in this framework, thanks to a two-modes cavity formalism leading to a Fabry-Perot formula, and show that a simplification can be made that preserves the accuracy of the results while allowing much simpler analytic expressions. For metallic layers thicker than 2.7 nm modified Fresnel coefficients can actually be used to accurately predict the response of any multilayer with spatially dispersive metals (for reflection, transmission or the guided modes). Finally, this explains why adding a small dielectric layer[Y. Luo et al., Phys. Rev. Lett. 111, 093901 (2013)] allows to reproduce the effects of nonlocality in many cases, and especially for multilayers.

physics.optics↗

arXiv2016

Leveraging beam deformation to improve the detection of resonances

Decades of work on beam deformation on reflection, and especially on lateral shifts, have spread the idea that a reflected beam is larger than the incident beam. However, when the right conditions are met, a beam reflected by a multilayered resonant structure can be 10\% narrower than the incoming beam. Such an easily measurable change occurs on a very narrow angular range close to a resonance, which can be leveraged to improve the resolution of sensors based on the detection of surface plasmon resonances by a factor three. We provide theoretical tools to deal with this effect, and a thorough physical discussion that leads to expect similar phenomenon to occur for temporal wavepackets and in other domains of physics.

physics.optics↗

arXiv2016

Universal metamaterial absorbe

We propose a design for an universal absorber, characterized by a resonance frequency that can be tuned from visible to microwave frequencies independently of the choice of the metal and the dielectrics involved. An almost resonant perfect absorption up to 99.8 % is demonstrated at resonance for all polarization states of light and for a very wide angular aperture. These properties originate from a magnetic Fabry-Perot mode that is confined in a dielectric spacer of $λ/100$ thickness by a metamaterial layer and a mirror. An extraordinary large funneling through nano-slits explains how light can be trapped in the structure. Simple scaling laws can be used as a recipe to design ultra-thin perfect absorbers whatever the materials and the desired resonance wavelength, making our design truly universal.

physics.optics↗

arXiv2016

Influence of spatial dispersion in metals on the optical response of deeply subwavelength slit arrays

In the framework of the hydrodynamic model describing the response of electrons in a metal, we show that arrays of very narrow and shallow metallic slits have an optical response that is influenced by the spatial dispersion in metals arising from the repulsive interaction between electrons. As a simple Fabry-Perot model is not accurate enough to describe the structure's behavior, we propose to consider the slits as generalized cavities with two modes, one being propagative and the other evanescent. This very general model allows to conclude that the impact of spatial dispersion on the propagative mode is the key factor explaining why the whole structure is sensitive to spatial dispersion. As the fabrication of such structures with relatively large gaps compared to previous experiments is within our reach, this work paves the way for future much needed experiments on nonlocality.

physics.optics↗