Deep Learning for On-Demand Hyperbolic Dispersion in Metasurfaces

Abstract

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Frequency dispersion is critical in designing metasurfaces, which enables advanced functionalities such as enhanced light-matter interaction and tailored wave propagation. In this paper, we demonstrated a deep learning approach for on-demand engineering of hyperbolic dispersion in metasurfaces. A deep neural network was trained to establish a direct mapping between pixelated metasurface structures and their electric and magnetic susceptibilities across a range of frequencies and polarizations. Utilizing this trained network, we performed inverse design to create a hyperbolic metasurface exhibiting a unique capability, exciting electric or magnetic hyperbolic polaritons according to source polarization. This work provides an innovative methodology for achieving precise control over hyperbolic dispersion and holds potential as a reusable, versatile inverse design tool, paving the way for novel wave manipulation strategies and advanced metamaterial devices.