Deep-learning design of graphene metasurfaces for quantum control and Dirac electron holography

TL;DR

This paper introduces a deep-learning framework for designing graphene-based metasurfaces to control electronic waves, enabling high-fidelity wave reconstruction and advanced functionalities like holography.

Contribution

It presents a novel deep-learning approach for Dirac-material metasurface design, achieving high wave reconstruction fidelity and enabling new electronic wave control applications.

Findings

Achieved over 95% fidelity in wave reconstruction

Demonstrated feasibility of Dirac electron holography

Enabled design of broadband and multi-functional graphene metasurfaces

Abstract

Metasurfaces are sub-wavelength patterned layers for controlling waves in physical systems. In optics, meta-surfaces are created by materials with different dielectric constants and are capable of unconventional functionalities. We develop a deep-learning framework for Dirac-material metasurface design for controlling electronic waves. The metasurface is a configuration of circular graphene quantum dots, each created by an electric potential. Employing deep convolutional neural networks, we show that the original scattering wave can be reconstructed with fidelity over 95$\%$, suggesting the feasibility of Dirac electron holography. Additional applications such as plane wave generation, designing broadband, and multi-functionality graphene metasurface systems are illustrated.