Abstract
The graphene-based Field Effect Transistors (GFETs), due to their multi-parameter characteristics, are growing rapidly as an important detection component for the apt detection of disease biomarkers, such as DNA, in clinical diagnostics and biomedical research laboratories. In this paper, the non-equilibrium Green function (NEGF) is used to create a compact model of GFET in the ballistic regime as an important building block for DNA detection sensors. In the proposed method, the self-consistent solutions of two-dimensional Poisson’s equation and NEGF, using the nearest neighbor tight-binding approach on honeycomb lattice structure of graphene, are modeled as an efficient numerical method. Then, the eight parameters of the phenomenological ambipolar virtual source (AVS) circuit model are calibrated by a least-square curve-fitting routine optimization algorithm with NEGF transfer function data. At last, some parameters of AVS that are affected by induced charge and potential of DNA biomolecules are optimized by an experimental dataset. The new compact model response, with an acceptable computational complexity, shows a good agreement with experimental data in reaction with DNA and can effectively be used in the plan and investigation of GFET biosensors.
Highlights
The early diagnosis of diseases such as viral infections and cancer cell disorders is crucial and significantly improves patient survival
To create an accurate and time-saving algorithm, in this paper, a compact numerical model is developed by a combination of non-equilibrium Green function (NEGF) for graphene FET modeling and the ambipolar virtual source (AVS) model for considering charge and potential effects of DNA biomolecule to study the possibility of realizing a graphene-based Field Effect Transistors (GFETs) as a biosensor detector
We show some instances where in the proposed compact model can be used in healthcare fields to realize the response of GFET that is functionalized as a receptor of biological markers, such as DNA from living cells
Summary
The early diagnosis of diseases such as viral infections and cancer cell disorders is crucial and significantly improves patient survival. In [9], an efficient numerical approach was proposed for modeling of transport of armchair graphene ribbon This method is based on an envelope function in the reciprocal space and a recursive matrix approach that the computation time was decreased with respect to the finite difference method. In [12], a liquidgated GFET based biosensor model is analytically developed for Escherichia Coli O157:H7 bacteria detection by simulation of its effects on the graphene surface in the form of conductance variation. To create an accurate and time-saving algorithm, in this paper, a compact numerical model is developed by a combination of NEGF for graphene FET modeling and the AVS model for considering charge and potential effects of DNA biomolecule to study the possibility of realizing a GFET as a biosensor detector. The developed transport model has been validated by comparing it with previously reported simulation results and experimental data
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