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      Deep learning enabled inverse design in nanophotonics

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          Abstract

          Deep learning has become the dominant approach in artificial intelligence to solve complex data-driven problems. Originally applied almost exclusively in computer-science areas such as image analysis and nature language processing, deep learning has rapidly entered a wide variety of scientific fields including physics, chemistry and material science. Very recently, deep neural networks have been introduced in the field of nanophotonics as a powerful way of obtaining the nonlinear mapping between the topology and composition of arbitrary nanophotonic structures and their associated functional properties. In this paper, we have discussed the recent progress in the application of deep learning to the inverse design of nanophotonic devices, mainly focusing on the three existing learning paradigms of supervised-, unsupervised-, and reinforcement learning. Deep learning forward modelling i.e. how artificial intelligence learns how to solve Maxwell’s equations, is also discussed, along with an outlook of this rapidly evolving research area.

          Most cited references5

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          Flat optics with designer metasurfaces.

          Conventional optical components such as lenses, waveplates and holograms rely on light propagation over distances much larger than the wavelength to shape wavefronts. In this way substantial changes of the amplitude, phase or polarization of light waves are gradually accumulated along the optical path. This Review focuses on recent developments on flat, ultrathin optical components dubbed 'metasurfaces' that produce abrupt changes over the scale of the free-space wavelength in the phase, amplitude and/or polarization of a light beam. Metasurfaces are generally created by assembling arrays of miniature, anisotropic light scatterers (that is, resonators such as optical antennas). The spacing between antennas and their dimensions are much smaller than the wavelength. As a result the metasurfaces, on account of Huygens principle, are able to mould optical wavefronts into arbitrary shapes with subwavelength resolution by introducing spatial variations in the optical response of the light scatterers. Such gradient metasurfaces go beyond the well-established technology of frequency selective surfaces made of periodic structures and are extending to new spectral regions the functionalities of conventional microwave and millimetre-wave transmit-arrays and reflect-arrays. Metasurfaces can also be created by using ultrathin films of materials with large optical losses. By using the controllable abrupt phase shifts associated with reflection or transmission of light waves at the interface between lossy materials, such metasurfaces operate like optically thin cavities that strongly modify the light spectrum. Technology opportunities in various spectral regions and their potential advantages in replacing existing optical components are discussed.
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            Inverse design in nanophotonics

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              Metamaterials: a new frontier of science and technology.

              Metamaterials, artificial composite structures with exotic material properties, have emerged as a new frontier of science involving physics, material science, engineering and chemistry. This critical review focuses on the fundamentals, recent progresses and future directions in the research of electromagnetic metamaterials. An introduction to metamaterials followed by a detailed elaboration on how to design unprecedented electromagnetic properties of metamaterials is presented. A number of intriguing phenomena and applications associated with metamaterials are discussed, including negative refraction, sub-diffraction-limited imaging, strong optical activities in chiral metamaterials, interaction of meta-atoms and transformation optics. Finally, we offer an outlook on future directions of metamaterials research including but not limited to three-dimensional optical metamaterials, nonlinear metamaterials and "quantum" perspectives of metamaterials (142 references).
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                Author and article information

                Journal
                Nanophotonics
                Walter de Gruyter GmbH
                2192-8614
                2192-8606
                February 17 2020
                February 17 2020
                : 9
                : 5
                : 1041-1057
                Affiliations
                [1 ]Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
                [2 ]Department Física Teórica de la Materia Condensada and Condensed Matter Physics Center (IFIMAC), Universidad Autónoma de Madrid, 28049-Madrid, Spain
                [3 ]Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
                Article
                10.1515/nanoph-2019-0474
                7ede59cf-40ec-4d77-903e-b34b071aec8d
                © 2020

                http://creativecommons.org/licenses/by/4.0

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