Dual gate AlGaN/GaN MOS-HEMT biosensor for electrical detection of biomolecules-analytical model

This paper proposes an analytical model for a dual gate AlGaN/GaN Metal oxide semiconductor-high-electron-mobility transistor (MOS-HEMT) biosensor for electrical detection of neutral species such as Biotin, Keratin, ChOx, and Zein. When only one subband is occupied and the AlGaN layer is assumed to have been fully ionized, the Fermi-Dirac statistic and 2D state density are used to produce a self-consistent calculation of the carrier density in the quantum well at the interface. It is done by analyzing the impact of biomolecule concentration by inserting a biomolecule of appropriate dielectric permittivity in the cavity area beneath the gate region. The impact of cavity length has been analyzed on the sensor's performance. The proposed device significantly changes the channel potential, transconductance, drain current, and threshold voltage. Dual gate structures offer superior resistance to short channel effects. Due to enhanced transport characteristics, high carrier mobility, drain current, and a variety of other factors, double gate MOS HEMT outperforms single-gate MOS HEMT. The maximal transconductance, drain on sensitivity, and the maximal drain current that has been attained in this work is 0.017 s, 0.22 and 0.129 mA, respectively, for biomolecule concentration, N (b) = 3 x 10(12). Among all the biomolecules used in this study, Keratin has achieved the maximum shift in threshold voltage and transconductance of 0.4 V and 0.016 s. The increase in current for Keratin, Biotin, Zein, and ChOx is 0.67%, 78%, 17%, and 42%, respectively, from single to dual gate AlGaN/GaN MOS-HEMT. SiO2, Al2O3, and HfO2 oxides have been compared by filling them in the left side of the cavity. Dual gate AlGaN/GaN MOS-HEMT biosensor presents an opportunity to develop robust, low-cost, specific detection and analysis of neutral biomolecule. The analytical model provides good results for drain current according to the comparison of simulation and analytical model findings.