Broadband Forward Light Scattering by Architectural Design of Core–Shell Silicon Particles

Marco, Maria Letizia De; Jiang, Taizhi; Fang, Jie; Lacomme, Sabrina; Zheng, Yuebing; Baron, Alexandre; Korgel, Brian A.; Barois, P.; Drisko, Glenna L.; Aymonier, Cyril

doi:10.1002/adfm.202100915

Cited by 19 publications

(14 citation statements)

References 45 publications

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“…For the transition from shorter to longer wavelengths of the MD relative to the ED, the network suggests core-shell nano-spheres transiting from high-index core and lower index shell to high-index shell with a lower index core. We note that the solution with lower index shell and higher index core for spectrally overlapping ED and MD are in agreement with recently proposed designs [43]. We observe that the network doesn't manage to find geometries which have a magnetic dipole resonance at shorter wavelengths than the electric dipole resonance, which is the expected physical behavior for dielectric nanostructures [54,3].…”

Section: Inverse Design Of Relative Resonance Positionssupporting

confidence: 87%

“…Practically, together with homogeneous spheres, core-shell nano-spheres are the most relevant spherical nano-particles. Precise chemical synthesis of nano-spheres with more than one coating layer is increasingly difficult, most practical applications are therefore limited to homogeneous structures or core-shell geometries [43].…”

Section: Design Problem Data Generation Deep Learning Modelsmentioning

confidence: 99%

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Inverse design with flexible design targets via deep learning: Tailoring of electric and magnetic multipole scattering from nano-spheres

Estrada‐Real¹,

Abdourahman²,

Urbaszek³

et al. 2022

Preprint

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Deep learning is a promising, ultra-fast approach for inverse design in nano-optics, but despite fast advancement of the field, the computational cost of dataset generation, as well as of the training procedure itself remains a major bottleneck. This is particularly inconvenient because new data need to be generated and a new network needs to be trained for any modification of the problem. We propose a technique that allows to train a single neural network on a broad range of design targets without any re-training. The key idea of our method is to enrich existing data with random "regions of interest" (ROI) labels. A model trained on such ROI-decorated data becomes capable to operate on a broad range of physical targets, while it learns to focus its design effort on a user-defined ROI, ignoring the rest of the physical domain. We demonstrate the method by training a tandem-network on the design of dielectric core-shell nano-spheres for electric and magnetic dipole and quadrupole scattering over a broad spectral range. The network learns to tailor very distinct, flexible design targets like scattering due to specific multipoles in narrow spectral windows. Varying the design problem does not require any re-training. Our approach is very general and can be directly used with existing datasets. It can be straightforwardly applied to other network architectures and problems.

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Section: Inverse Design Of Relative Resonance Positionssupporting

confidence: 87%

Section: Design Problem Data Generation Deep Learning Modelsmentioning

confidence: 99%

Inverse design with flexible design targets via deep learning: Tailoring of electric and magnetic multipole scattering from nano-spheres

Estrada‐Real¹,

Abdourahman²,

Urbaszek³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…These last works 61,62,66 announce the upcoming large-scale availability of crystalline silicon nanospheres, which opens the way to future applications of AOM in metasurfaces or metamaterials.…”

Section: 7d)mentioning

confidence: 99%

The Bottom-Up Approach toward Artificial Optical Magnetism in Metastructures

Aradian

Barois

Mondain‐Monval

et al. 2021

Hybrid Flatland Metastructures

Self Cite

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The generation of artificial optical magnetism (AOM) in engineered composites has been the major concept that led to the emergence of the field of metamaterials at the turn of the millennium. Indeed, the proven possibility to manipulate the magnetic permeability of materials at microwave frequencies induced a considerable excitement in the scientific community as it opened the way to the design of unprecedented tools and devices for the control of light propagation. Extensions to higher frequencies of IR and visible light were soon proposed and tested by downsizing the artificial structures. The fabrication of negative index materials, optical cloaks or hyper-lenses seemed within reach. Two decades later, and after considerable research efforts, the applications of the AOM are still scarce and the concept seems to face a number of fundamental physical limits. We review in this chapter the state-of-the-art of the bottom-up approach whereby nanochemistry and colloidal physics are used to engineer hybrid metastructures exhibiting AOM in visible light or near IR.

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“…45 A Huygens' metasurface exploits the so-called Kerker effect resulting in forward-only scattering, to achieve unitary transmission. 3,[46][47][48] The permittivity of silicon is taken from literature, 49 the disc is placed in air.…”

Section: Visualize Spatial Sensitivity To Multipole Mode Excitationmentioning

confidence: 99%

Generalizing the exact multipole expansion: Density of multipole modes in complex photonic nanostructures

Majorel,

Patoux,

Estrada-Real

et al. 2022

Preprint

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The multipole expansion of a nano-photonic structure's electromagnetic response is a versatile tool to interpret optical effects in nano-optics, but it requires knowledge of the internal field distribution, for which in general expensive full-field simulations are necessary. We present a generalized polarizability model based on the Green's Dyadic Method (GDM), capable to describe resonant nanostructures, supporting both electric and magnetic dipole and quadrupole modes. Our formalism combines the recently developed exact multipole decomposition [Alaee et al., Opt. Comms. 407, 17-21 (2018)] with the concept of a generalized field propagator. It reproduces the exact multipole moments for any particle size and for arbitrary, inhomogeneous illumination fields such as focused vector beams or local light sources. After an initial computation step, our approach allows to instantaneously obtain the exact multipole decomposition for any possible illumination, and it allows to calculate spectra of the total density of multipole modes. Furthermore, the technique can be used to visualize spatial zones inside a nanostructure, where an external illumination couples strongly to a specific multipole moment. The formalism will be very useful for various applications in nano-optics like illumination-field engineering, or meta-atom design e.g. for Huygens metasurfaces. We provide a numerical open source implementation compatible with the pyGDM python package.

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Broadband Forward Light Scattering by Architectural Design of Core–Shell Silicon Particles

Cited by 19 publications

References 45 publications

Inverse design with flexible design targets via deep learning: Tailoring of electric and magnetic multipole scattering from nano-spheres

Inverse design with flexible design targets via deep learning: Tailoring of electric and magnetic multipole scattering from nano-spheres

The Bottom-Up Approach toward Artificial Optical Magnetism in Metastructures

Generalizing the exact multipole expansion: Density of multipole modes in complex photonic nanostructures

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