Detection of SARS-CoV-2 using dielectric modulated TFET-based biosensor

Area of Science:

• Nanotechnology and Biosensing
• Medical Diagnostics
• Virology

Background:

• The rapid spread of Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has been declared a pandemic by the WHO.
• Early diagnosis and effective management are crucial for controlling the COVID-19 outbreak.
• Existing diagnostic methods require improvement in sensitivity and speed for timely detection.

Purpose of the Study:

• To develop and investigate a novel biosensor for the detection of SARS-CoV-2.
• To analyze the sensor's performance in detecting viral proteins (spike, envelope) and DNA.
• To evaluate the sensor's sensitivity, noise performance, and dependence on DNA charge density.

Main Methods:

• A biosensor based on a vertical tunnel field-effect transistor (VTFET) architecture was designed and fabricated.
• Detection mechanism relies on analyzing the shift in drain current upon hybridization of viral biomolecules (proteins, DNA) in nanogaps.
• The dielectric constant equivalent of virus proteins and DNA charge density were used to model sensor response.
• Sensitivity, threshold voltage, noise performance, and the impact of interface trap charges were analyzed.

Main Results:

• The proposed VTFET biosensor demonstrated exceptionally high sensitivity, on the order of 10^6, indicating superior detection capabilities.
• Sensitivity was found to be significantly influenced by DNA charge density, with variations affecting threshold voltage and overall sensitivity.
• The sensor's noise performance was investigated, and its sensitivity was evaluated both with and without interface trap charges.

Conclusions:

• The developed VTFET biosensor is a highly sensitive and viable platform for detecting SARS-CoV-2 from clinical samples.
• The sensor's performance highlights its potential as a superior alternative to existing FET-based biosensors for viral detection.
• Further investigation into DNA charge density and interface effects can optimize sensor performance for clinical applications.