Scalable Tight-Binding Model for Graphene

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-Perot 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.
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Submitted on : Wednesday, March 14, 2018 - 11:54:16 AM
Last modification on : Tuesday, July 9, 2019 - 4:52:33 PM

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Ming-Hao Liu, Peter Rickhaus, Peter Makk, Endre Tovari, Romain Maurand, et al.. Scalable Tight-Binding Model for Graphene. Physical Review Letters, American Physical Society, 2015, 114 (3), pp.036601. ⟨10.1103/PhysRevLett.114.036601⟩. ⟨cea-01731470⟩

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