Atomistic modeling of graphene nanostructures and single-electron quantum dots
Abstract
The technological advancement in the semiconductor industry in the past few decades has mainly been driven by the continuous down-scaling of CMOS devices following Moore's Law. However, device scaling is fundamentally limited by a number of technological issues, such as short-channel effects. The length scale of today's device is quickly approaching its fundamental limit. As a result, extensive research efforts are invested to find new materials and novel device concepts to enhance device performance. Computational modeling serves as a highly effective and efficient approach in exploring these materials and device concepts. This work utilizes several computational modeling methods to study two types of devices (a) graphene based nanostructures, and (b) silicon based quantum dots. The first part of this thesis is focused on modeling a new class of graphene based nanostructures, namely graphene nanomesh (GNM). It is a 2D nanostructure obtained by fabricating periodic perforations on a piece of graphene sheet. According to experimental studies, this is an effective approach of tuning the bandgap. This work applies an advanced nearest-neighbor tight-binding model in the electronic structure calculation of GNM. The model details as well as its advantages compared to the conventional model are discussed. Transport properties of GNM are investigated using non-equilibrium Green's function (NEGF) method. Two types of hole geometries in GNM, circular and rectangular holes, are studied in details. It is demonstrated that bandstructures of GNM can be engineered via hole geometry. Some intriguing features in the GNM bandstructures are observed, such as dispersionless bands, edge localization of electron wavefunctions, anisotropic dispersion relations. The effects of edge geometries (zigzag vs. armchair) in GNM on its electronic structure and transport properties are investigated. Lastly, some novel device applications that utilize these unique electronic properties of GNM are proposed. The second part of this work is focused on predicting valley splitting (VS) in an electrostatically defined silicon quantum dot. The significance of VS for quantum information storage is explained. Eigenstates of the quantum dot have been calculated charge self-consistently and the VS has been extracted under various biasing conditions. Simulation results indicate that the VS in this quantum dot can be controlled by tuning the potential barrier and gate geometry. Full range of the achievable VS is determined. This information is vital in guiding the design of Si quantum dots with desired VS.
Degree
M.S.E.C.E.
Advisors
Klimeck, Purdue University.
Subject Area
Electrical engineering|Nanotechnology
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