High Sensitivity of Dielectrically Modulated Tunnel Field Effect Transistor for Biosensor Applications

The Dielectrically Modulated Full Gate Tunnel Field Effect Transistor (FET) with dual nanocavities, as described in the paper, is a novel device designed as a label-free biosensor for detecting cancer cell biomolecules. This biosensor utilizes the principles of field-effect transistors and incorporates nanocavities to enhance the detection sensitivity. The simulations are conducted using the Silvaco Atlas model, which allowed for the analysis of the device's electrical characteristics in the presence of various cancer cell biomolecules. The performance of the proposed device is evaluated using several sensing metrics, including current, threshold voltage, and subthreshold slope. These metrics are examined to assess their sensitivity to the presence of different cancer cell biomolecules. By analyzing these electrical characteristics, we can able to determine the device's ability to detect and differentiate between specific biomolecules associated with cancer cells. One important aspect discussed in the paper is the incorporation of nanocavities in the device design. These nanocavities have a significant impact on enhancing the sensing capabilities of the biosensor. The paper also introduces the concept of the filling factor parameter, which describes the fraction of the nanocavity volume occupied by the cancer cell biomolecules. This parameter plays a crucial role in achieving optimal sensing performance. Overall, the paper presents a comprehensive analysis of the proposed Dielectrically Modulated Full gate Tunnel FET embedded with dual nanocavities as a label-free biosensor for cancer cell biomolecules. The simulations conducted using the Silvaco Atlas model provide valuable insights into the device's electrical characteristics and its sensitivity to different biomolecules. The study emphasizes the significance of nanocavities and their filling factor parameter in achieving enhanced sensing performance for cancer cell detection.