Synergizing Deep Learning and Phase Change Materials for Four-state Broadband Multifunctional Metasurfaces in the Visible Range

TL;DR

This paper introduces broadband four-state metasurfaces in the visible range by combining phase change materials and neural network inverse design, enabling multiple optical functionalities in a compact device.

Contribution

The work presents the first broadband four-state metasurfaces with multiple functionalities using cascaded phase change materials and neural network-based inverse design.

Findings

Achieved broadband reflection switching among four states in visible range.

Demonstrated multiple functionalities: achromatic deflection, beam splitting, focusing, broadband absorption.

Validated inverse-designed metasurfaces for portable medical imaging applications.

Abstract

In this article, we report, for the first time, broadband multifunctional metasurfaces with more than four distinct functionalities. The constituent meta-atoms combine two different phase change materials, $\mathrm{VO_2}$ and $\mathrm{Sb_2S_3}$ in a multi-stage configuration. FDTD simulations demonstrate a broadband reflection amplitude switching between the four states in visible range due to the enhanced cavity length modulation effect from the cascaded Fabry-Perot cavities, overcoming the inherent small optical contrast between the phase change material (PCM) states. This, along with the reflection phase control between the four states, allows us to incorporate both amplitude and phase-dependent properties in the same metasurface - achromatic deflection, wavelength beam splitting, achromatic focusing, and broadband absorption, overcoming the limitations of previous functionality switching mechanisms for the visible band. We have used a Tandem Neural network-based inverse design scheme to ensure the stringent requirements of different states are realized. We have used two forward networks for predicting the reflection amplitude and phase for a meta-atom within the pre-defined design space. The excellent prediction capability of these surrogate models is utilized to train the reverse network. The inverse design network, trained with a labeled data set, is capable of producing the optimized meta-units given the desired figure-of-merits in terms of reflection amplitude and phase for the four states. The optical characteristics of two inverse-designed metasurfaces have been evaluated as test cases for two different sets of design parameters in the four states. Both structures demonstrate the four desired broadband functionalities while closely matching the design requirements, suggesting their potential in visible-range portable medical imaging devices.