Drain current sensitivity analysis using a surface potential-based analytical model for AlGaN/GaN double gate MOS-HEMT

Abstract

In this research, a surface potential-based drain current model for an AlGaN/GaN symmetrical double-gate metal oxide semiconductor high electron mobility transistor (DG-MOSHEMT) is proposed and used to detect label-free biomolecules for the first time. The model is applied for a nanocavity-based DG-MOSHEMT and works on the concept of dielectric modulation to detect biomolecules like uricase, urease, streptavidin, protein, biotin, ChOx, and APTES(3-aminopropyltriethoxysilane). The performance indicators, such as a change in drain ON current (ΔION), drain current sensitivity (SION), transconductance sensitivity (Sgm), and output conductance sensitivity (Δgd), are used to assess the device's efficacy. Numerical simulations are conducted to verify the model's accuracy and quantify its sensitivity. The DG-MOSHEMT device elicited exceptional sensitivity towards the uricase biomolecule, surpassing all other analytes tested, with SION = 0.328, Sgm = 0.197, and Δgd = 28.1 mS/mm. Furthermore, the model considers the contribution of the first two sub-bands of the quantum well and competently captures the fluctuation in drain ON current and sensitivity. A comprehensive sensitivity analysis of the drain current is conducted by altering physical parameters such as cavity length, thickness, gate length and barrier layer thickness. Notably, the sensitivity SION is significantly improved with cavity length and thickness for the optimized AlGaN barrier layer.