The Graphene Field Effect Transistor Modeling Based on an Optimized Ambipolar Virtual Source Model for DNA Detection

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.

Two-Dimensional Field-Effect Transistor Sensors: The Road toward Commercialization

“…Current electrochemical techniques for detecting nucleic acids have some barriers because they often require chemical labeling, expensive reagents, and highly trained experimenters . As comparison, 2D FET nucleic acid sensors are of great interest due to their label-free and direct detection ability after a hybridization event. , Over the past decade, researchers developed a variety of 2D FET nucleic acid sensors with different operation principles such as chemical doping, , chemical/electrostatic gating, , capacitive model, , Hall effect measurement, scattering effect, etc. Here, we summarize three representative applications of 2D FET nucleic acid sensors, which include quantitative detection, monitoring of genome mutations, and clinical diagnosis.…”

“…Machine learning (ML), the branch of artificial intelligence, is defined as computer programs that find hidden correlations and acquire knowledge in large data sets. , ML models are used to provide credible prediction, classification, and decision for complex data, thus being a powerful tool to build smart and high-performance systems. , The most important features of ML are sample categorization, noise reduction, data reprocessing, and object identification/decision. , The workflow presented in Figure summarizes the general processes of ML models designed for material science and the sensing field. , …”

Dual-band electromagnetically induced transparency terahertz metamaterial based on U-like resonator and metallic cut wire for sensing application

“…In addition, metastructures [17], epsilon-near zero MMs have emerged [18], and the use of nanoparticles allows the preparation of epsilon-negative metamaterials [19] as well as promises to help characterize MMs [20]. The combination of nanomaterials with biomolecules (e.g., graphene/DNA) has led to rapid developments in clinical diagnostics and biomedicine [21], and therefore the combination of MMs with nanoparticles will allow the applications of MMs to be expanded further. At the same time, theories are being developed to analyze these resonance properties, such as the electric/thermal circuit model [22], Green's Function [23], field and circuit theory [24].…”

A compact high gain filtering patch antenna for multiband wireless system

“…The sensing performance of epsilon near zero metamaterial is analyzed by developing a transmission line model [16]. The genetic disease diagnosis using a graphene field effect transistor is illustrated in [17] which provides rapid and accurate diagnosis of DNAs. Openended metamaterial [18] assemblies having anisotropic plasmonic building blocks enhance optical magnetic field with chiral plasmonics.…”