Topology optimization of grating couplers for the efficient excitation of surface plasmons

Andkjær, Jacob Anders; Nishiwaki, Shinji; Nomura, Tsuyoshi; Sigmund, Ole

doi:10.1364/josab.27.001828

Cited by 88 publications

(50 citation statements)

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“…For example, it is possible to set the conditions for the input (for example, the mode of an optical fiber) and output (for example, the mode of a metallic nanowire) electromagnetic field profiles and then to ask a computer to determine the geometric distribution of metal and dielectric inside the IOD that provides the desired coupling efficiency. Such problems can be solved with the algorithms of topological optimization [24] and the optimal geometry can be very sophisticated though realistic for fabrication.…”

Section: Discussionmentioning

confidence: 99%

“…Для широкого волновода была предложена решетка с коэффициентом ввода 68% [24]. Система из нескольких соединенных плазмонных антенн была рассчитана численно и экспериментально измерена при помощи сканирующего ближнепо-левого оптического микроскопа [25].…”

Section: Iods On the Basis Of Nanoantennasunclassified

“…For a wide waveguide the grating with CE = 68% was suggested [24]. The system of оптические устройства и системы several connected plasmonic antennas was modelled numerically and characterized experimentally with a scanning near-field optical microscope [25].…”

Section: Iods On the Basis Of Diffraction Gratingsmentioning

confidence: 99%

“…Действи-тельно, можно задать условия на распределение входного (например, мода оптического волокна) и выходного (например, мода металлического нанопровода) электромагнитного поля и пору-чить компьютеру определение геометрического расположения металла и диэлектрика внутри УВВ, которое обеспечивает требуемый коэффи-циент ввода. Подобные задачи решает тополо-гическая оптимизация [24], при этом оптималь-ная геометрия устройств может быть весьма замысловатой, но реалистичной с точки зрения изготовления.…”

Section: заключениеunclassified

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In- and out-coupling devices for subwavelength waveguides. Part 2

Andryieuski¹

2016

FRos

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Section: Discussionmentioning

confidence: 99%

Section: Iods On the Basis Of Nanoantennasunclassified

Section: Iods On the Basis Of Diffraction Gratingsmentioning

confidence: 99%

Section: заключениеunclassified

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In- and out-coupling devices for subwavelength waveguides. Part 2

Andryieuski¹

2016

FRos

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“…Hence, control of these material properties allows wave manipulation for various purposes, e.g., creating reflection bands for coloration. Here we work with a TiO 2 substrate with a nanostructure composed of TiO 2 and SiO 2 ; however, the developed topology optimization methodology can handle any simple (linear, homogeneous, isotropic) material combinations including metallic structures [25] and also frequency-dependent material properties. The method works by varying the distribution of materials within a bounded design domain in order to optimize certain responses of the physical system.…”

Section: Inverse Design Methodologymentioning

confidence: 99%

Inverse design of nanostructured surfaces for color effects

et al. 2013

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We propose an inverse design methodology for systematic design of nanostructured surfaces for color effects. The methodology is based on a 2D topology optimization formulation based on frequency-domain finite element simulations for E and/or H polarized waves. The goal of the optimization is to maximize color intensity in prescribed direction(s) for a prescribed color (RGB) vector. Results indicate that nanostructured surfaces with any desirable color vector can be generated; that complex structures can generate more intense colors than simple layerings; that angle independent colorings can be obtained at the cost of reduced intensity; and that performance and optimized surface topologies are relatively independent on light polarization.

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Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy

et al. 2019

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mostly relies on physics-inspired methods, resorting to human knowledge such as physical insights revealed by simplified analytical modeling, similar experience transferred from previous practice, and intuition obtained by scientific reasoning. For example, many meta-atoms inherited traditional antenna designs with geometries like rectangle, [4] cross, [5] bowtie, [6] V-shape, [7] H-shape, [8] and so on, whose first-order response is approximated by electrical dipole resonance with relevant scaling effect. [9] Some other designs guided by physical intuition include ring-like structures that exhibit strong magnetic resonances induced by the incident magnetic field, [10][11][12] dielectric building blocks that can induce both electric and magnetic resonances leading to better control of the phase of the scattered light, [13][14][15] or the spectra line-shape tailoring by introducing coupling among different resonant modes. [16][17][18] Despite the exciting results obtained by these physics-inspired designs, this methodology basically relies on a trial-and-error process, usually involving numerical methods like finite-difference-time-domain (FDTD) or finite element method (FEM) to iteratively solve Maxwell's equations. The low efficiency and thus limited exploration of the design varieties tend to easily omit the optimal solution. The inverse design approaches start from the opposite end, and try to optimize certain objective functions describing the desired performance. [19,20] Common approaches for inverse problems include genetic algorithm, [21] level set methods, [22] and topology optimization, [23] which, however, are still stochastic searching algorithms that are time-consuming and deteriorate rapidly as the design space grows. Different from numerical calculations, data-driven methods based on machine learning (ML) solve the optimization problem from statistical perspectives, so that the solution to optimize a target can be approximately generalized from numerous design examples. With the rapidly accumulated data and thus booming of deep learning (DL), the state-of-theart in many research domains, such as speech recognition, [24] computer vision, [25,26] natural language processing, [27] and decision making, [28] has been pushed far beyond conventional methods. Deep neural networks simulate biological signal processing that allow computational models to learn multiple The research of metamaterials has achieved enormous success in the manipulation of light in a prescribed manner using delicately designed subwavelength structures, so-called meta-atoms. Even though modern numerical methods allow for the accurate calculation of the optical response of complex structures, the inverse design of metamaterials, which aims to retrieve the optimal structure according to given requirements, is still a challenging task owing to the nonintuitive and nonunique relationship between physical structures and optical responses. To better unveil this implicit relationship and thus facilitate metamaterial designs, it is proposed ...

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Topology optimization of grating couplers for the efficient excitation of surface plasmons

Cited by 88 publications

References 24 publications

In- and out-coupling devices for subwavelength waveguides. Part 2

In- and out-coupling devices for subwavelength waveguides. Part 2

Inverse design of nanostructured surfaces for color effects

Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy

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