光子学
纳米光子学
超材料
硅光子学
波导管
光子超材料
光电子学
栅栏
光学
光子晶体
物理
材料科学
作者
Pavel Cheben,Jens H. Schmid,Robert Halir,José Manuel Luque‐González,J. Gonzalo Wangüemert‐Pérez,Daniele Melati,Carlos Alonso-Ramos
出处
期刊:Advances in Optics and Photonics
[The Optical Society]
日期:2023-11-08
卷期号:15 (4): 1033-1033
被引量:17
摘要
Since the invention of the silicon subwavelength grating waveguide in 2006, subwavelength metamaterial engineering has become an essential design tool in silicon photonics. Employing well-established nanometer-scale semiconductor manufacturing techniques to create metamaterials in optical waveguides has allowed unprecedented control of the flow of light in photonic chips. This is achieved through fine-tuning of fundamental optical properties such as modal confinement, effective index, dispersion, and anisotropy, directly by lithographic imprinting of a specific subwavelength grating structure onto a nanophotonic waveguide. In parallel, low-loss mode propagation is readily obtained over a broad spectral range since the subwavelength periodicity effectively avoids losses due to spurious resonances and bandgap effects. In this review we present recent advances achieved in the surging field of metamaterial integrated photonics. After briefly introducing the fundamental concepts governing the propagation of light in periodic waveguides via Floquet–Bloch modes, we review progress in the main application areas of subwavelength nanostructures in silicon photonics, presenting the most representative devices. We specifically focus on off-chip coupling interfaces, polarization management and anisotropy engineering, spectral filtering and wavelength multiplexing, evanescent field biochemical sensing, mid-infrared photonics, and nonlinear waveguide optics and optomechanics. We also introduce a nascent research area of resonant integrated photonics leveraging Mie resonances in dielectrics for on-chip guiding of optical waves, with the first Huygens’ metawaveguide recently demonstrated. Finally, we provide a brief overview of inverse design approaches and machine-learning algorithms for on-chip optical metamaterials. In our conclusions, we summarize the key developments while highlighting the challenges and future prospects.
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