Integrated photonic metasystem for image classifications at telecommunication wavelength
Date
2022-04-19
Journal Title
Journal ISSN
Volume Title
Publisher
Nature Communications
Abstract
Miniaturized image classifiers are potential for revolutionizing their applications in optical communication, autonomous vehicles, and healthcare. With subwavelength structure enabled directional diffraction and dispersion engineering, the light propagation through multi-layer metasurfaces achieves wavelength-selective image recognitions on a silicon photonic platform at telecommunication wavelength. The metasystems implement high-throughput vector-by-matrix multiplications, enabled by near 103 nanoscale phase shifters as weight elements within 0.135 mm2 footprints. The diffraction manifested computing capability incorporates the fabrication and measurement related phase fluctuations, and thus the pre-trained metasystem can handle uncertainties in inputs without post-tuning. Here we demonstrate three functional metasystems: a 15-pixel spatial pattern classifier that reaches near 90% accuracy with femtosecond inputs, a multi-channel wavelength demultiplexer, and a hyperspectral image classifier. The diffractive metasystem provides an alternative machine learning architecture for photonic integrated circuits, with densely integrated phase shifters, spatially multiplexed throughput, and data processing capabilities.
Description
© The Author(s) 2022. This article was originally published in Nature Communications. The version of record is available at: https://doi.org/10.1038/s41467-022-29856-7
Keywords
metamaterials, silicon photonics
Citation
Wang, Z., Chang, L., Wang, F. et al. Integrated photonic metasystem for image classifications at telecommunication wavelength. Nat Commun 13, 2131 (2022). https://doi.org/10.1038/s41467-022-29856-7