Since its isolation and characterization by Novoselov et al. in 2004, graphene, a monolayer of carbon atoms with a relatively low atomic mass, has captured significant interest and attention from scientists all over the world. This is primarily due to its unique properties, such as weak spin-orbit coupling, the absence of hyperfine interactions, and exceptionally high carrier mobility. This high mobility results from graphene’s zero bandgap and linear dispersion relation at the Dirac cones, with a Fermi velocity of 10^6 m/s. By exploring its transport properties, graphene can be harnessed to develop high-performance quantum devices for various applications, including molecular sensors, bolometers, and spin qubits, the building blocks of quantum computers.

In this context, we employ a computational framework designed to predict the properties of graphene-based devices, such as nanoribbons and quantum dots. Based on non-equilibrium green’s function, density functional theory, and tight binding model. This framework enables studies ranging from gate-level spectroscopy to shot noise and charge transport processes, accommodating different configurations and setups for a wide range of applications. It offers a powerful tool for tuning and optimizing these devices prior to experimental implementation, paving the way for improved engineering designs and a deeper understanding of their operation.