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

Abstract

Metasurfaces are sub-wavelength patterned layers for controlling waves in physical systems. In optics, metasurfaces 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 and designing broadband and multi-functionality electronic metasurface in graphene are illustrated.