Electronic transport and spatial current patterns of 2D electronic system: A recursive Green’s function method study

Zhang, X. W.; Liu, Y. L.

doi:10.1063/1.5130534

Cited by 14 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We would like to point out that the recursive procedure has to be employed for a large central device to save computing time and for calculating the surface Green's function of leads. The related details can be referred elsewhere [39,40] and we do not repeat here.…”

Section: Models and Computational Methodsmentioning

confidence: 99%

Effects of antidot lattices density on transport features in zigzag graphene nanoribbons

Zhang¹,

Liu²

2022

Electron. Struct.

View full text Add to dashboard Cite

Creating antidot lattices in graphene nanoribbons (GNRs) can significantly modify the electronic transport features and may open up an avenue to many practical applications. We here study the effects of antidot lattices on two-terminal transport in GNRs with zigzag edges (ZGNRs), based on the tight-binding method in combination with Green’s function formalism. The antidots in this work are set to be hexagonal structure. For the case of two antidots arranging in ZGNRs, many conductance resonances are found and they become denser and shaper with the increasing of the separation between antidots. However, no any effective transport gap is observed around Fermi energy. For the case of multi-antidots structure, each resonance shows a (m-1)-splitting, where m is the number of antidots. The analysis on local density of states (LDOS) indicates that all of resonances are related to the quasi-standing waves in ZGNRs. To obtain an effective and stable transport gap, we suggest keeping a dense array of such antidots in ZGNRs. The computed results show that the transport gap decreases very rapidly as the separation between antidots increases. These results might guide the design of the future graphene-based devices.

show abstract

Section: Models and Computational Methodsmentioning

confidence: 99%

Effects of antidot lattices density on transport features in zigzag graphene nanoribbons

Zhang¹,

Liu²

2022

Electron. Struct.

View full text Add to dashboard Cite

show abstract

“…The training data consist of a large number of 'image functions' (calculated SGM patterns) of the different potentials, which are calculated using the standard recursive Green's function approach [42,43,45]. In particular, we divide the effective device region into several transverse segments and calculate the Green's function from the left and right leads.…”

Section: Generating Training Data Using the Green's Function Methodsmentioning

confidence: 99%

“…To adapt the CNNs to our quantum inverse problem, we first build up a theoretical database of potential-SGM image pairs by using the standard Green's function approach [42,43] and use them as our ground truth. We then train the CNN with the data set to make it learn how to translate the latter into the former.…”

Section: Introductionmentioning

confidence: 99%

A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning

Cunha

Aoki

Ferry

et al. 2022

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

The inverse problem of estimating the background potential from measurements of the local density of states (LDOS) is a challenging issue in quantum mechanics. Even harder is doing this estimation using approximate methods such as scanning gate microscopy (SGM). Here we propose a machine-learning-based solution by exploiting adaptive cellular neural networks (CNN). For the paradigmatic setting of a quantum point contact, the training data consist of potential-SGM functional relations represented by image pairs. These are generated by the recursive Green’s function method. We demonstrate that the CNN-based machine learning framework can predict the background potential corresponding to the experimental image data. This is aﬃrmed by analyzing the estimated potential with image processing techniques based on the comparison between the charge densities and those obtained from diﬀerent techniques. Correlation analysis of the images suggests the possibility of estimating diﬀerent contributions to the background potential. In particular, our results indicate that both charge puddles and ﬁxed impurities contribute to the spatial patterns found in the SGM data. Our work represents a timely contribution to the rapidly evolving ﬁeld of exploiting machine learning to solve diﬃcult problems in physics.

show abstract

“…The conductance can be evaluated from the transmission probabilities of up and down spin electrons following the Landauer definition, whereas spin-dependent transmission probabilities are computed with the help of the well known non-equilibrium Green’s function (NEGF) technique [ 80 ]. In the NEGF approach, the effects of the side attached electrodes are incorporated through finite dimensional self-energy matrices, and the effective Green’s functions are [ 80 , 81 , 82 , 83 , 84 ]

where E is the energy of an incident electron and I represents the identity matrix. All of the matrices in Equation ( 7 ) are of the dimension

.…”

Section: Magnetoresistance Setup and Theoretical Frameworkmentioning

confidence: 99%

Possible Routes to Obtain Enhanced Magnetoresistance in a Driven Quantum Heterostructure with a Quasi-Periodic Spacer

et al. 2021

View full text Add to dashboard Cite

In this work, we perform a numerical study of magnetoresistance in a one-dimensional quantum heterostructure, where the change in electrical resistance is measured between parallel and antiparallel configurations of magnetic layers. This layered structure also incorporates a non-magnetic spacer, subjected to quasi-periodic potentials, which is centrally clamped between two ferromagnetic layers. The efficiency of the magnetoresistance is further tuned by injecting unpolarized light on top of the two sided magnetic layers. Modulating the characteristic properties of different layers, the value of magnetoresistance can be enhanced significantly. The site energies of the spacer is modified through the well-known Aubry–André and Harper (AAH) potential, and the hopping parameter of magnetic layers is renormalized due to light irradiation. We describe the Hamiltonian of the layered structure within a tight-binding (TB) framework and investigate the transport properties through this nanojunction following Green’s function formalism. The Floquet–Bloch (FB) anstaz within the minimal coupling scheme is introduced to incorporate the effect of light irradiation in TB Hamiltonian. Several interesting features of magnetotransport properties are represented considering the interplay between cosine modulated site energies of the central region and the hopping integral of the magnetic regions that are subjected to light irradiation. Finally, the effect of temperature on magnetoresistance is also investigated to make the model more realistic and suitable for device designing. Our analysis is purely a numerical one, and it leads to some fundamental prescriptions of obtaining enhanced magnetoresistance in multilayered systems.

show abstract

Electronic transport and spatial current patterns of 2D electronic system: A recursive Green’s function method study

Cited by 14 publications

References 26 publications

Effects of antidot lattices density on transport features in zigzag graphene nanoribbons

Effects of antidot lattices density on transport features in zigzag graphene nanoribbons

A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning

Possible Routes to Obtain Enhanced Magnetoresistance in a Driven Quantum Heterostructure with a Quasi-Periodic Spacer

Contact Info

Product

Resources

About