Graphene nanoscroll, because of attractive electronic, mechanical, thermoelectric and optoelectronics properties, is a suitable candidate for transistor and sensor applications. In this research, the electrical transport characteristics of high-performance field effect transistors based on graphene nanoscroll are studied in the framework of analytical modeling. To this end, the characterization of the proposed device is investigated by applying the analytical models of carrier concentration, quantum capacitance, surface potential, threshold voltage, subthreshold slope and drain induced barrier lowering. The analytical modeling starts with deriving carrier concentration and surface potential is modeled by adopting the model of quantum capacitance. The effects of quantum capacitance, oxide thickness, channel length, doping concentration, temperature and voltage are also taken into account in the proposed analytical models. To investigate the performance of the device, the current-voltage characteristics are also determined with respect to the carrier density and its kinetic energy. According to the obtained results, the surface potential value of front gate is higher than that of back side. It is noteworthy that channel length affects the position of minimum surface potential. The surface potential increases by increasing the drain-source voltage. The minimum potential increases as the value of quantum capacitance increases. Additionally, the minimum potential is symmetric for the symmetric structure (Vfg = Vbg). In addition, the threshold voltage increases by increasing the carrier concentration, temperature and oxide thickness. It is observable that the subthreshold slope gets closer to the ideal value of 60 mV/dec as the channel length increases. As oxide thickness increases the subthreshold slope also increases. For thinner gate oxide, the gate capacitance is larger while the gate has better control over the channel. The analytical results demonstrate a rational agreement with existing data in terms of trends and values.
Read full abstract