Computationally efficient simulation method for conductivity modeling of 2D-based conductors

Rizzi, Leo; Zienert, Andreas; Schuster, Jörg; Köhne, Martin; Schulz, Stefan E.

doi:10.1016/j.commatsci.2019.02.022

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Further details are reported in Refs. [23,24]. We modeled our nanographite flakes as randomly shaped polygons with a surface area A and a thickness t flake .…”

Section: Methodsmentioning

confidence: 99%

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Anisotropic transport properties of graphene-based conductor materials

et al. 2021

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We analyzed nanographite-based materials in a combined study including experimental analysis via 4-point probe STM and simulation to provide a complete picture of microscopic and macroscopic properties of the material. The two- and three-dimensional transport regimes were determined and evaluated regarding the anisotropy of the conductivity. The experimental results yield the full macroscopic conductivity tensor. Microstructural simulations are used to map those macroscopic properties to the microscopic building blocks of the sample. By combining those two, we present a coherent and comprehensive description of the electrical material parameters across several length scales.

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“…Further details are reported in Refs. [23,24]. We modeled our nanographite flakes as randomly shaped polygons with a surface area A and a thickness t flake .…”

Section: Methodsmentioning

confidence: 99%

“…Also, transfer matrix methods for calculating a dynamical conductivity were reported [22]. Very recently, simulations based on random resistor networks were established to model systems made from 2D building blocks [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Anisotropic transport properties of graphene-based conductor materials

et al. 2021

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“…describing the influence of metal contacts [12][13][14][15] or imperfections [16][17][18][19][20][21][22]. For even larger systems like networks [23][24][25] or whole transistors [7] (semi)-classical approaches are used.…”

Section: Introductionmentioning

confidence: 99%

Quantum transport in graphene nanoribbon networks: complexity reduction by a network decimation algorithm

et al. 2023

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We study electronic quantum transport in graphene nanoribbon (GNR) networks on mesoscopic length scales. We focus on zigzag GNRs and investigate the conductance properties of statistical networks. To this end we use a density-functional-based tight-binding model to determine the electronic structure and quantum transport theory to calculate electronic transport properties. We then introduce a new efficient network decimation algorithm that reduces the complexity in generic GNR networks. We compare our results to semi-classical calculations based on the nodal analysis approach and discuss the dependence of the conductance on network density and network size. We show that a nodal analysis model cannot reproduce the quantum transport results and their dependence on model parameters well. Thus, solving the quantum network by our efficient approach is mandatory for accurate modelling the electron transport through GNR networks.

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“…[ 18 ] Very recently, Rizzi et al. [ 19,20 ] have proposed a computationally efficient resistor network model for graphene‐based conductor materials to calculate the overall electrical conductivity. Nonetheless, this and other methods require strong assumptions in the consideration of vertical transport: for example, the overlapping regions are collapsed into single points or external/fitting parameters are needed to model conductance between overlapping nanotubes.…”

Section: Introductionmentioning

confidence: 99%

“…These models, however, cannot be directly applied to the case of arrangements of quasi 2D objects, like graphene or transition metal dichalcogenide flakes, since additional complexities are introduced, such as the need of understanding vertical transport across overlapping areas between different flakes. [18] Very recently, Rizzi et al [19,20] have proposed a computationally efficient resistor network model for graphene-based conductor materials to calculate the overall electrical conductivity. Nonetheless, this and other methods require strong assumptions in the consideration of vertical transport: for example, the overlapping regions are collapsed into single points or external/fitting parameters are needed to model conductance between overlapping nanotubes.…”

Section: Introductionmentioning

confidence: 99%

Electronic Transport in 2D‐Based Printed FETs from a Multiscale Perspective

Perucchini

Marian

Marín

et al. 2022

Adv Elect Materials

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As 2D materials (2DMs) gain the research limelight as a technological option for obtaining on‐demand printed low‐cost integrated circuits with reduced environmental impact, theoretical methods able to provide the necessary fabrication guidelines acquire fundamental importance. Here, a multiscale modeling technique is exploited to study electronic transport in devices consisting of a printed 2DM network of flakes. The approach implements a Monte Carlo scheme to generate the flake distribution. By means of ab initio density functional theory calculations together with non equilibrium Green's functions formalism, detailed physical insights on flake‐to‐flake transport mechanisms are provided. This later feeds a 3D drift‐diffusion and Poisson solution to compute self‐consistently transport and electrostatics in the device. The method is applied to MoS2 and graphene‐based dielectrically gated FETs, highlighting the impact of the structure density and variability on the mobility and sheet resistance. The prediction capability of the proposed approach is validated against electrical measurements of in‐house printed graphene conductive lines as a function of film thickness, demonstrating its strong potential as a guide for future experimental activity in the field.

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Computationally efficient simulation method for conductivity modeling of 2D-based conductors

Cited by 6 publications

References 18 publications

Anisotropic transport properties of graphene-based conductor materials

Anisotropic transport properties of graphene-based conductor materials

Quantum transport in graphene nanoribbon networks: complexity reduction by a network decimation algorithm

Electronic Transport in 2D‐Based Printed FETs from a Multiscale Perspective

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