^{1}

^{2}

Nanowire field effect transistors can be modeled for ultrasensitive charge detection based bio- or chemical sensors. As critical dimensions of the nanowire sensor can be of the same order of size of biological molecules or chemical species yielding exceptional sensing possibilities. In addition, the large surface/volume ratio will give high sensitivities simply because surface effects dominate over bulk properties. Thus, we modeled Si nanowire with different geometries in the different chemical environment using NEGF approach. To analyze the performance, the sensitivity of Si nanowire with different cross sections including circular, rectangular, and triangular is derived by two definitions. It is calculated that the sensitivity of Si nanowire with different structures is a function of geometrical parameters and doping density. It is illustrated that the sensitivity varies inversely with cross-section area, doping density, and also the length of nanowire.

In recent years, silicon nanostructures have attracted great interest as a building block for micro-electro-mechanical systems (MEMS), nano-electro-mechanical systems (NEMS) and nano-electronic devices. For example, silicon nanostructures find applications in diverse areas such as sensors, bio-sensors, medical technology, and communication technologies [

Atomistic simulation methods such as first-principles quantum-mechanical methods [

In Section 2, we discuss the system model we used; Section 3 consists of NEGF-DFT formalism; in Section 4, we model the effect of encapsulation, & deformed structures, with results & conclusions in Section 5.

A general model for detecting molecules using Si-NWs is shown in

Due to the presence of native oxide on the NW surface, we assume that the negligible charge transfer is expected to take place between the molecule and the semiconductor. However, it is reported in [

Selectivity denotes the ability of receptors to bind with the desired target in the presence of various other (possibly similar) molecules and is entirely determined by the functionalization schemes [

The device model applied in the transport calculation consists of three parts, the studied material and two electrodes under bias V_{b}. Thus the Hamiltonian H for full systems can be of the form:

were H_{LL}_{/RR} are the Hamiltonian for left/right electrode and H_{CC} + H_{LC} + H_{RC} gives Hamiltonian for extended molecule, consisting of molecule in addition to three layer of surface atoms of two electrodes. Here, each term is represented as

were

Thus using

we get

Combining green function we get

Here

were

Similarly we calculate for I_{LL}, I_{RR}, I_{CC}, I_{CR}, etc.

Thus

Here S is the overlap matrix and I stands for identity operator. We describe overlap matrix is close to identity matrix, thus matrix calculation

The solution of G_{CC} is

where,

Tr means the trace analyzed. Thus using above equation we get transmission function of the systems. The electron transport calculations are performed using NEGF combined with DFT within the Landauer formalism [

where e, h, and f_{L}_{(R)} are electron charge, Planck’s constant, and the Fermi distribution functions at left (right) electrode, respectively. T(E, V_{b}) is the transmission coefficient at energy E and bias voltage V_{b}. We work with the Perdew-Zunger exchange and correlation functional [

On analysis we found that with the increase in diameter of SiNWs the band gap decreases and it is inversely proportional to the diameter of wire i.e.

From ^{2} to 1.5 nm^{2} for [

Our results are in agreements and perform the same trends with the experimental results [

As impurities and dopants are adsorbed on SiNW surface, so they influence the electronic structure, which causes the change in conductance/transport properties. We calculate band structures for nanowires doped with N, & -OH using ATOMISTIX TOOLKIT. Interestingly, the different dopant adsorbed, clearly resulted in different band structure. Thus, all the results shown in

sensors by tuning the band gaps through controlling surface density of dopants/surface treatments.

Lamba, V.K. and Garg, O.P. (2016) Designing Sensors Using Nano-Junctions. Journal of Applied Mathematics and Physics, 4, 2247-2253. http://dx.doi.org/10.4236/jamp.2016.412217