Deriving a novel methodology for nano-BioFETs and analysing the effect of high-k oxides on the amino-acids sensing application

In this paper, we present simulation results of a BioFET using methodology based on the Gouy-Chapman-Stern and the Site-binding models.The derived simulation approach is used to sense different amino acids, such as Arginine (R), Aspartic Acid (D) and Proline (P), functionalized with the help of a linker connected to the gateoxide.The performance of the BioFETs is optimized while analyzing the effect of high-k dielectrics as the gate oxide.In general, the channel oxides are responsible for tuning the parameters such as sensitivity, surface potential and intrinsic buffer capacity.The variation of total surface capacitance, the second derivative of drain current and surface potential are used to uniquely identify the signatures of different amino acids.The proposed method can be helpful in defining an efficient method for label-free protein sequencing.