New trends in nanophotonics

Sunae So, Namkyoo Park, Hak Joo Lee, Junsuk Rho
2020 Nanophotonics  
Nanophotonics considers the complex interactions between light and matter at the sub-wavelength scale. Recent progress in nanophotonics has revealed unprecedented optical phenomena, which have opened up the novel and rapidly developing fields of metamaterials, photonic crystals, and plasmonics. The last few decades have seen explosive growth in this field, from fundamental research to applications including condensed-matter physics, quantum photonics, near-field/far-field optics, biochemical
more » ... ics, biochemical sensing, deep learning for nanophotonic design, and nanofabrication/ nanomanufacturing. The International Conference on Metamaterials, Photonic Crystals, and Plasmonics (META) is an annual conference of researchers in metamaterials, nanophotonics, and other closely related topics. It covers a broad range of topics including, but not limited to, metasurfaces, meta-devices, topological effects in optics, two-dimensional materials, light-matter interaction in nano-cavities, plasmonic circuits, thermal engineering, and quantum photonic systems. The latest conference, META'19, was held in Lisbon, Portugal (July 23-26, 2019), where the latest trends and recent progress in nanophotonics were discussed to provide further insights for researchers. This special issue introduces a selection of cutting-edge original research and review papers from the conference. Metasurfaces can manipulate the optical properties of light with the ultrathin materials. Two groups review this topic in detail in this issue. Wei et al. [1] focus on the recent research progress in metasurfaces for holographic displays, polarization conversion, active modulation, and linear and nonlinear modulation; the authors discuss in detail the working principle and advantages of metasurfaces and provide many specific applications. Intaravanne and Chen [2] review the recent progress on metasurfaces, mainly focusing on metasurface devices for polarization detection and polarization profiles. The review also presents various applications of metasurface-based devices by manipulating arbitrary polarizations, with specific examples for high-resolution images, quick-response codes, color images, and holograms. Other topics on metasurfaces deal with the unique metasurface-driven optical properties, presented by three groups. Mun et al. [3] report a method to modulate the phase information of a Fano-resonant metasurface; they present an optical de-multiplexer that covers a broad wavelength range in the near-infrared region, and experimentally verify the extremely small full width at half-maximum. The proposed design approach can provide complete control of Fanoresonant metasurfaces for various applications including optical multiplexers, filters, and switches. Park et al. [4] present a metasurface deflector that can control the direction of emission of colloidal quantum dots; the design of metasurfaces was adapted from the optical Yagi-Uda nanoantenna, and the deflection efficiency of the device reaches up to 70%. The developed metasurface facilitates the integration of metasurfaces into a resonant nanocavity, which can lead to the rapid development of many active devices. As another metasurface device, Park et al. [5] demonstrate a general non-Hermitian metasurface to observe exceptional points that exhibit both complex eigenvalues and eigenvectors of a system to form defective eigen-spaces. The designed metasurface is composed of two orthogonally oriented split-ring resonators in a unit cell and exhibits an exceptional point in the polarization space of light. These results provide a platform to study non-Hermitian physics and sensitive terahertz systems that have nontrivial phase responses. This issue also presents the combination of nanophotonics with deep learning, which has seen an explosion in interest recently. Specifically, the combination of deep learning and inverse design in nanophotonics is reviewed comprehensively by So et al. [6], who discuss recent progress in the application of deep learning to inverse design in nanophotonic devices, as categorized by three paradigms of the learning method. In addition, Jiang and Fan [7] present a global optimization algorithm that uses deep learning to design metasurfaces. The proposed optimization method considers populations and a generative neural network that is trained to optimize the population of the devices. The process allows efficient global optimization, which can be extended to general inverse design problems in other areas of physics.
doi:10.1515/nanoph-2020-0170 fatcat:fi5mei7v3jgbvcbotwbakitwmq