Metasurface on integrated photonic platform: from mode converters to machine learning
Date
2022-07-20
Journal Title
Journal ISSN
Volume Title
Publisher
Nanophotonics
Abstract
Integrated photonic circuits are created as a stable and small form factor analogue of fiber-based optical systems, from wavelength-division multiplication transceivers to more recent mode-division multiplexing components. Silicon nanowire waveguides guide the light in a way that single and few mode fibers define the direction of signal flow. Beyond communication tasks, on-chip cascaded interferometers and photonic meshes are also sought for optical computing and advanced signal processing technology. Here we review an alternative way of defining the light flow in the integrated photonic platform, using arrays of subwavelength meta-atoms or metalines for guiding the diffraction and interference of light. The integrated metasurface system mimics free-space optics, where on-chip analogues of basic optical components are developed with foundry compatible geometry, such as low-loss lens, spatial-light modulator, and other wavefront shapers. We discuss the role of metasurface in integrated photonic signal processing systems, introduce the design principles of such metasurface systems for low loss compact mode conversion, mathematical operation, diffractive optical systems for hyperspectral imaging, and tuning schemes of metasurface systems. Then we perceive reconfigurability schemes for metasurface framework, toward optical neural networks and analog photonic accelerators.
Description
This article was originally published in Nanophotonics. The version of record is available at: https://doi.org/10.1515/nanoph-2022-0294
Keywords
deep learning, metasurface, silicon photonics
Citation
Wang, Zi, Xiao, Yahui, Liao, Kun, Li, Tiantian, Song, Hao, Chen, Haoshuo, Uddin, S. M. Zia, Mao, Dun, Wang, Feifan, Zhou, Zhiping, Yuan, Bo, Jiang, Wei, Fontaine, Nicolas K., Agrawal, Amit, Willner, Alan E., Hu, Xiaoyong and Gu, Tingyi. "Metasurface on integrated photonic platform: from mode converters to machine learning" Nanophotonics , no. (2022). https://doi.org/10.1515/nanoph-2022-0294