Design rules for interconnects based on graphene nanoribbon junctions
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- 1. Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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Description
Graphene nanoribbons (GNRs) produced by means of bottom-up chemical self-assembly are considered promising candidates for the next-generation nanoelectronic devices. We address the electronic transport properties of angled two-terminal GNR junctions, which are inevitable in the interconnects in graphene-based integrated circuits. We construct a library of over 400000 distinct configurations of 60° and 120° junctions connecting armchair GNRs of different widths. Numerical calculations combining the tight-binding approximation and the Green's function formalism allow identifying numerous junctions with conductance close to the limit defined by the GNR leads. Further analysis reveals underlying structure-property relationships with crucial roles played by the bipartite symmetry of graphene lattice and the presence of resonant states localized at the junction. In particular, we discover and explain the phenomenon of binary conductance in 120° junctions connecting metallic GNR leads that guarantee maximum possible conductance. Overall, our study defines the guidelines for engineering GNR junctions with desired electrical properties.
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References
Journal reference (Paper in which the data is discussed) K. Čerņevičs, O. V. Yazyev (submitted) (2021)
Website (Web application for calculating electronic transport properties of graphene nanoribobon junctions.) Kristiāns Čerņevičs, Valeria Granata, Oleg V. Yazyev, Tight-Binding Electronic Transport Application (TBETA) for graphene nanoribbon junctions, Materials Cloud Archive 2020.66 (2020), doi: 10.24435/materialscloud:av-24
Website (Web application for calculating electronic transport properties of graphene nanoribobon junctions.) Kristiāns Čerņevičs, Valeria Granata, Oleg V. Yazyev, Tight-Binding Electronic Transport Application (TBETA) for graphene nanoribbon junctions, Materials Cloud Archive 2020.66 (2020)