Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces

Abstract

Metasurfaces have provided a novel and promising platform for the realization of compact and large-scale optical devices. The conventional metasurface design approach assumes periodic boundary conditions for each element, which is inaccurate in most cases since the near-field coupling effects between elements will change when surrounded by non-identical structures. In this paper, we propose a deep learning approach to predict the actual electromagnetic (EM) responses of each target meta-atom placed in a large array with near-field coupling effects taken into account. The predicting neural network takes the physical specifications of the target meta-atom and its neighbors as input, and calculates its phase and amplitude in milliseconds. This approach can be applied to explain metasurfaces' performance deterioration caused by mutual coupling and further used to optimize their efficiencies once combined with optimization algorithms. To demonstrate the efficacy of this methodology, we obtain large improvements in efficiency for a beam deflector and a metalens over the conventional design approach. Moreover, we show the correlations between a metasurface's performance and its design errors caused by mutual coupling are not bound to certain specifications (materials, shapes, etc.). As such, we envision that this approach can be readily applied to explore the mutual coupling effects and improve the performance of various metasurface designs.

16 pages, 10 figures

References in corpus (10)

• Dielectric Metasurfaces for Complete Control of Phase and Polarization with Subwavelength Spatial Resolution and High Transmission
• Training deep neural networks for the inverse design of nanophotonic structures
• A Generative Model for Inverse Design of Metamaterials
• Miniature optical planar camera based on a wide-angle metasurface doublet corrected for monochromatic aberrations
• Achromatic Metalens in the Visible and Metalens with Reverse Chromatic Dispersion
• Reconfigurable all-dielectric metalens with diffraction limited performance
• Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks
• Topology optimization of freeform large-area metasurfaces
• Multifunctional Metasurface Design with a Generative Adversarial Network
• A Hybrid Strategy for the Discovery and Design of Photonic Nanostructures