Design and Performance Analysis of N+ Pocket-Doped Vertical DMDGDP TFET With T-Shape Channel for Sensitivity Improvement—As a Biosensor
Girija Sravani Kondaveeti, Rapolu Anil Kumar, K. Srinivasa Rao
Abstract
This article presents and simulates N+ pocket-doped vertical dielectrically modulated double gate–drain pocket (DP) tunnel field-effect transistor (V-DMDGDP TFET), a highly sensitive label-free biosensor. For the first time, a T-shaped channel is employed in a biosensor using a Triple Hetero junction. The proposed V-DMDGDP TFET device is composed of semiconductors such as Silicon (Si), Silicon Dioxide (SiO2), Hafnium Oxide (HfO2), and Titanium Oxide (TiO2). Using a sacrificial etching approach two cavities are formed in the gate–source junction areas and two additional cavities are formed in the gate–drain junction regions for the detection of biomolecules such as SARS COV2 (Covid cells, K = 4), Carbohydrates (K = 5), RNA (K = 8), and Amino acids (K = 12). 2-D calibrated simulations were used to examine how several device characteristics such as the height and length of the cavity affected different performance measures. Greater dielectric constants produce better drain current values, which enhance the device sensitivity. The biosensor sensitivity to both charged and neutral biological molecules can be significantly enhanced by optimizing the cavity length and height. For neutral, positive, and negative charged biomolecules, the proposed biosensor sensitivity has been greatly increased to 3.68 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> 105, 2.13 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> 106, and 3.97 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> 103, respectively. Comparing it with other advanced biosensors, it has demonstrated improved sensitivity. Thus, the device is used in identifying SARS-COV2, Carbohydrates, RNA, Amino acids, and so on.