arXiv subjects

Peter R. Wiecha

Publications and source records attributed to Peter R. Wiecha.

36 records · Page 2Linked to original sources

arXiv2021

Hyper-doped silicon nanoantennas and metasurfaces for tunable infrared plasmonics

We present the experimental realization of ordered arrays of hyper-doped silicon nanodisks, which exhibit a localized surface plasmon resonance. The plasmon is widely tunable in a spectral window between 2 and 5 $μ$m by adjusting the free carrier concentration between 10$^{20}$ and 10$^{21}$ cm$^{-3}$. We show that strong infrared light absorption can be achieved with all-silicon plasmonic metasurfaces employing nano-structures with dimensions as low as 100\,nm in diameter and 23 nm in height. Our numerical simulations show an excellent agreement with the experimental data and provide physical insights on the impact of the nanostructure shape as well as of near-field effects on the optical properties of the metasurface. Our results open highly promising perspectives for integrated all-silicon-based plasmonic devices for instance for chemical or biological sensing or for thermal imaging.

cond-mat.mes-hall↗

arXiv2021

Deep learning in nano-photonics: inverse design and beyond

Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nanostructures. Many of the recent works on machine-learning inverse design are highly specific, and the drawbacks of the respective approaches are often not immediately clear. In this review we want therefore to provide a critical review on the capabilities of deep learning for inverse design and the progress which has been made so far. We classify the different deep learning-based inverse design approaches at a higher level as well as by the context of their respective applications and critically discuss their strengths and weaknesses. While a significant part of the community's attention lies on nano-photonic inverse design, deep learning has evolved as a tool for a large variety of applications. The second part of the review will focus therefore on machine learning research in nano-photonics "beyond inverse design". This spans from physics informed neural networks for tremendous acceleration of photonics simulations, over sparse data reconstruction, imaging and "knowledge discovery" to experimental applications.

physics.optics↗

arXiv2020

Deep learning enabled design of complex transmission matrices for universal optical components

Recent breakthroughs in photonics-based quantum, neuromorphic and analogue processing have pointed out the need for new schemes for fully programmable nanophotonic devices. Universal optical elements based on interferometer meshes are underpinning many of these new technologies, however this is achieved at the cost of an overall footprint that is very large compared to the limited chip real estate, restricting the scalability of this approach. Here, we consider an ultracompact platform for low-loss programmable elements using the complex transmission matrix of a multi-port multimode waveguide. We propose a deep learning inverse network approach to design arbitrary transmission matrices using patterns of weakly scattering perturbations. The demonstrated technique allows control over both the intensity and phase in a multiport device at a four orders reduced device footprint compared to conventional technologies, thus opening the door for large-scale integrated universal networks.

physics.optics↗

arXiv2020

Unveiling the optical emission channels of monolayer semiconductors coupled to silicon nanoantennas

Monolayers (MLs) of transition metal dichalcogenides (TMDs) such as WSe2 and MoSe2 can be placed by dry stamping directly on broadband dielectric resonators, which have the ability to enhance the spontaneous emission rate and brightness of solid-state emitters at room temperature. We show strongly enhanced emission and directivity modifications in room temperature photoluminescence mapping experiments. By varying TMD material (WSe2 versus MoSe2) transferred on silicon nanoresonators with various designs (planarized versus non-planarized), we experimentally separate the different physical mechanisms that govern the global light emission enhancement. For WSe2 and MoSe2 we address the effects of Mie Resonances and strain in the monolayer. For WSe2 an important additional contribution comes from out-of-plane exciton dipoles. This paves the way for more targeted designs of TMD-Si nanoresonator structures for room temperature applications.

cond-mat.mes-hall↗

arXiv2020

Quantum Theory of Near-field Optical Imaging with Rare-earth Atomic Clusters

Scanning near-field optical imaging (SNOM) using local active probes provides in general images of the electric part of the photonic local density of states. However, certain atomic clusters can supply more information by simultaneously revealing both the magnetic (m-LDOS) and the electric (e-LDOS) local density of states in the optical range. For example, nanoparticles doped with rare-earth elements like europium or terbium provide both electric dipolar (ED) and magnetic dipolar (MD) transitions. In this theoretical article, we develop a quantum description of active systems (rare earth ions) coupled to a photonic nanostructure, by solving the optical Bloch equations together with Maxwell's equations. This allows us to access the population of the emitting energy levels for all atoms excited by the incident light, degenerated at the extremity of the tip of a near-field optical microscope. We show that it is possible to describe the collected light intensity due to ED and MD transitions in a scanning configuration. By carrying out simulations on different experimentally interesting systems, we demonstrate that our formalism can be of great value for the interpretation of experimental configurations including various external parameters (laser intensity, polarization and wavelength, the SNOM probe size, the nature of the sample ...).

cond-mat.mes-hall↗

arXiv2020

Polarizabilities of complex individual dielectric or plasmonic nanostructures

When the sizes of photonic nanoparticles are much smaller than the excitation wavelength, their optical response can be efficiently described with a series of polarizability tensors. Here, we propose a universal method to extract the different components of the response tensors associated with small plasmonic or dielectric particles. We demonstrate that the optical response can be faithfully approximated, as long as the effective dipole is not induced by retardation effects, hence do not depend on the phase of the illumination. We show that the conventional approximation breaks down for a phase-driven dipolar response, such as optical magnetic resonances in dielectric nanostructures. To describe such retardation induced dipole resonances in intermediate-size dielectric nanostructures, we introduce "pseudo-polarizabilities" including first-order phase effects, which we demonstrate at the example of magnetic dipole resonances in dielectric spheres and ellipsoids. Our method paves the way for fast simulations of large and inhomogeneous meta-surfaces.

cond-mat.mes-hall↗

arXiv2019

Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures

Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal methods for design and analysis of nanophotonic systems.

physics.comp-ph↗

arXiv2019

Design of plasmonic directional antennas via evolutionary optimization

We demonstrate inverse design of plasmonic nanoantennas for directional light scattering. Our method is based on a combination of full-field electrodynamical simulations via the Green dyadic method and evolutionary optimization (EO). Without any initial bias, we find that the geometries reproducibly found by EO, work on the same principles as radio-frequency antennas. We demonstrate the versatility of our approach by designing various directional optical antennas for different scattering problems. EO based nanoantenna design has tremendous potential for a multitude of applications like nano-scale information routing and processing or single-molecule spectroscopy. Furthermore, EO can help to derive general design rules and to identify inherent physical limitations for photonic nanoparticles and metasurfaces.

physics.app-ph↗

arXiv2019

Enhancement of electric and magnetic dipole transition of rare-earth doped thin films tailored by high-index dielectric nanostructures

We propose a simple experimental technique to separately map the emission from electric and magnetic dipole transitions close to single dielectric nanostructures, using a few nanometer thin film of rare-earth ion doped clusters. Rare-earth ions provide electric and magnetic dipole transitions of similar magnitude. By recording the photoluminescence from the deposited layer excited by a focused laser beam, we are able to simultaneously map the electric and magnetic emission enhancement on individual nanostructures. In spite of being a diffraction-limited far-field method with a spatial resolution of a few hundred nanometers, our approach appeals by its simplicity and high signal-to-noise ratio. We demonstrate our technique at the example of single silicon nanorods and dimers, in which we find a significant separation of electric and magnetic near-field contributions. Our method paves the way towards the efficient and rapid characterization of the electric and magnetic optical response of complex photonic nanostructures.

cond-mat.mes-hall↗

arXiv2018

Pushing the limits of optical information storage using deep learning

Diffraction drastically limits the bit density in optical data storage. To increase the storage density, alternative strategies involving supplementary recording dimensions and robust read-out schemes must be explored. Here, we propose to encode multiple bits of information in the geometry of subwavelength dielectric nanostructures. A crucial problem in high-density information storage concepts is the robustness of the information readout with respect to fabrication errors and experimental noise. Using a machine-learning based approach in which the scattering spectra are analyzed by an artificial neural network, we achieve quasi error free read-out of sequences of up to 9 bit, encoded in top-down fabricated silicon nanostructures. We demonstrate that probing few wavelengths instead of the entire spectrum is sufficient for robust information retrieval and that the readout can be further simplified, exploiting the RGB values from microscopy images. Our work paves the way towards high-density optical information storage using planar silicon nanostructures, compatible with mass-production ready CMOS technology.

physics.app-ph↗

arXiv2018

pyGDM -- A python toolkit for full-field electro-dynamical simulations and evolutionary optimization of nanostructures

pyGDM is a python toolkit for electro-dynamical simulations in nano-optics based on the Green Dyadic Method (GDM). In contrast to most other coupled-dipole codes, pyGDM uses a generalized propagator, which allows to cost-efficiently solve large monochromatic problems such as polarization-resolved calculations or raster-scan simulations with a focused beam or a quantum-emitter probe. A further peculiarity of this software is the possibility to very easily solve 3D problems including a dielectric or metallic substrate. Furthermore, pyGDM includes tools to easily derive several physical quantities such as far-field patterns, extinction and scattering cross-section, the electric and magnetic near-field in the vicinity of the structure, the decay rate of quantum emitters and the LDOS or the heat deposited inside a nanoparticle. Finally, pyGDM provides a toolkit for efficient evolutionary optimization of nanoparticle geometries in order to maximize (or minimize) optical properties such as a scattering at selected resonance wavelengths.

physics.comp-ph↗

arXiv2018

Decay Rate of Magnetic Dipoles near Non-magnetic Nanostructures

In this article, we propose a concise theoretical framework based on mixed field-susceptibilities to describe the decay of magnetic dipoles induced by non--magnetic nanostructures. This approach is first illustrated in simple cases in which analytical expressions of the decay rate can be obtained. We then show that a more refined numerical implementation of this formalism involving a volume discretization and the computation of a generalized propagator can predict the dynamics of magnetic dipoles in the vicinity of nanostructures of arbitrary geometries. We finally demonstrate the versatility of this numerical method by coupling it to an evolutionary optimization algorithm. In this way we predict a structure geometry which maximally promotes the decay of magnetic transitions with respect to electric emitters.

physics.optics↗

arXiv2018

Fano-Resonances in High Index Dielectric Nanowires for Directional Scattering

High refractive index dielectric nanostructures provide original optical properties thanks to the occurrence of size- and shape-dependent optical resonance modes. These modes commonly present a spectral overlap of broad, low-order modes (\textit{e.g}. dipolar modes) and much narrower, higher-order modes. The latter are usually characterized by a rapidly varying frequency-dependent phase, which - in superposition with the lower order mode of approximately constant phase - leads to typical spectral features known as Fano resonances. Interestingly, such Fano resonances occur in dielectric nanostructures of the simplest shapes. In spheroidal nanoparticles, interference between broad magnetic dipole and narrower electric dipole modes can be observed. In high aspect-ratio structures like nanowires, either the electric or the magnetic dipolar mode (depending on the illumination conditions) interferes with higher order multipole contributions of the same nature (electric or magnetic). Using the analytical Mie theory, we analyze the occurrence of Fano resonances in high-index dielectric nanowires and discuss their consequences like unidirectional scattering. By means of numerical simulations, we furthermore study the impact on those Fano resonances of the shape of the nanowire cross-sections as well as the coupling of two parallel nanowires. The presented results show that all-dielectric nanostructures, even of simple shapes, provide a reliable low-loss alternative to plasmonic nanoantennas.

physics.app-ph↗

arXiv2017

Strongly directional scattering from dielectric nanowires

It has been experimentally demonstrated only recently that a simultaneous excitation of interfering electric and magnetic resonances can lead to uni-directional scattering of visible light in zero-dimensional dielectric nanoparticles. We show both theoretically and experimentally, that strongly anisotropic scattering also occurs in individual dielectric nanowires. The effect occurs even under either pure transverse electric or pure transverse magnetic polarized normal illumination. This allows for instance to toggle the scattering direction by a simple rotation of the incident polarization. Finally, we demonstrate that directional scattering is not limited to cylindrical cross-sections, but can be further tailored by varying the shape of the nanowires.

cond-mat.mes-hall↗

arXiv2017

Local field enhancement and thermoplasmonics in multimodal Aluminum structures

Aluminum nanostructures have recently been at the focus of numerous studies due to their properties including oxidation stability and surface plasmon resonances covering the ultraviolet and visible spectral windows. In this article, we reveal a new facet of this metal relevant for both plasmonics purpose and photo-thermal conversion. The field distribution of high order plasmonic resonances existing in two-dimensional Al structures is studied by nonlinear photoluminescence (nPL) microscopy in a spectral region where electronic interband transitions occur. The polarization sensitivity of the field intensity maps shows that the electric field concentration can be addressed and controlled on-demand. We use a numerical tool based on the Green dyadic method to analyze our results and to simulate the absorbed energy that is locally converted into heat. The polarization-dependent temperature increase of the Al structures is experimentally quantitatively measured, and is in an excellent agreement with theoretical predictions. Our work highlights Al as a promising candidate for designing thermal nanosources integrated in coplanar geometries for thermally assisted nanomanipulation or biophysical applications.

cond-mat.mes-hall↗

arXiv2017

Evolutionary Multi-Objective Optimisation of Colour Pixels based on Dielectric Nano-Antennas

The rational design of photonic nanostructures consists in anticipating their optical response from simple models or as variations of reference systems. This strategy is limited when different objectives are simultaneously targeted. Inspired from biology, evolutionary approaches drive the morphology of a nano-object towards an optimum through several cycles of selection, mutation and cross-over, mimicking the process of natural selection. However, their extension to scenarii with multiple objectives demands efficient computational schemes. We present a numerical technique to design photonic nanostructures with optical properties optimized along several arbitrary objectives. This combination of evolutionary multi-objective algorithms with frequency-domain electro-dynamical simulations is used to design silicon nanostructures resonant at user-defined, polarization-dependent wavelengths. The spectra of pixels fabricated by electron beam lithography following the optimized design show excellent agreement with the targeted objectives. The method is self-adaptive to arbitrary constraints, and therefore particularly interesting for the design of complex structures within technological limits.

cond-mat.mes-hall↗

arXiv2017

Enhanced nonlinear optical response from individual silicon nanowires

We report about the experimental observation and characterization of nonlinear optical properties of individual silicon nanowires of different dimensions. Our results show that the nonlinear light has different components, one of them corresponding to the second harmonic generation (SHG). The SHG strongly depends on the polarization of the optical excitation and nanowire diameter, and gives access to the local electromagnetic field intensity distribution. Furthermore, we show that the second harmonic, when observed, is enhanced compared to bulk silicon and is sensitive to optical resonances supported by the nanowires. This offers different perspectives on the definition of silicon-based nonlinear photonic devices.

cond-mat.mes-hall↗

arXiv2016

Origin of Second Harmonic Generation from individual Silicon Nanowires

We investigate Second Harmonic Generation from individual silicon nanowires and study the influence of resonant optical modes on the far-field nonlinear emission. We find that the polarization of the Second Harmonic has a size-dependent behavior and explain this phenomenon by a combination of different surface and bulk nonlinear susceptibility contributions. We show that the Second Harmonic Generation has an entirely different origin, depending on whether the incident illumination is polarized parallel or perpendicularly to the nanowire axis. The results open perspectives for further geometry-based studies on the origin of Second Harmonic Generation in nanostructures of high-index centrosymmetric semiconductors.

cond-mat.mes-hall↗