Scalable Tight-Binding Model for Graphene

TL;DR

This paper introduces a scalable tight-binding model for graphene that accurately reproduces transport properties, enabling efficient simulations of complex devices and predicting new conductance phenomena.

Contribution

The authors derive a simple condition for band structure invariance in scaled graphene lattices and demonstrate its effectiveness through transport simulations matching experimental results.

Findings

Transport features like Fabry-Pérot interference and quantum Hall effect are well reproduced by the scaled model.

The model allows for reduced computational complexity in simulating graphene devices.

Predictions of unexplored conductance quantization in graphene are demonstrated.

Abstract

Artificial graphene consisting of honeycomb lattices other than the atomic layer of carbon has been shown to exhibit electronic properties similar to real graphene. Here, we reverse the argument to show that transport properties of real graphene can be captured by simulations using "theoretical artificial graphene." To prove this, we first derive a simple condition, along with its restrictions, to achieve band structure invariance for a scalable graphene lattice. We then present transport measurements for an ultraclean suspended single-layer graphene pn junction device, where ballistic transport features from complex Fabry-P\'erot interference (at zero magnetic field) to the quantum Hall effect (at unusually low field) are observed and are well reproduced by transport simulations based on properly scaled single-particle tight-binding models. Our findings indicate that transport simulations for graphene can be efficiently performed with a strongly reduced number of atomic sites, allowing for reliable predictions for electric properties of complex graphene devices. We demonstrate the capability of the model by applying it to predict so-far unexplored gate-defined conductance quantization in single-layer graphene.