Dual Material Double Gate Graphene Nanoribbon Vertical Tunnel FETs Gas sensor and Circuit level Applications

Abstract

The advancement in CMOS technology has led to growing apprehensions about escalating power dissipation, primarily stemming from the constraints of MOSFET switches. Tackling the challenge of heightened power dissipation requires the development of small-signal switches capable of surpassing the Boltzmann limit of 60mV/decade. In this scenario, TFET emerges as a promising avenue for investigating the benefits of band-to-band tunneling current conduction. Furthermore, enhancing efficiency can be achieved by replacing conventional Silicon with Graphene Nanoribbon material, thereby reducing tunneling width and consequently enhancing device performance. This thesis focuses on optimizing conventional TFET structures to enhance ON-current and minimize ambipolar current. To achieve this, a 2D material, Graphene Nanoribbon (GNR), is utilized as the channel material for the proposed TFET. The device leverages Double-Gate and Dual-MaterialGate configurations to enhance ON-current and OFF-current, respectively. Additionally, high-k material is selected as the gate dielectric to enhance the transfer characteristics of the TFET. The performance of the proposed TFET, based on GNR material, is compared with a similar structure employing Silicon material, particularly for low-power applications. The investigation utilizes TCAD simulation, calibrated to closely match experimental findings.Two analytical modeling approaches have been devised for the proposed device. The first approach utilizes a semi-classical current transport model to analyze the drain current model for Double-Gate (DG) Dual-Material-Gate (DMG) Graphene-Nanoribbon (GNR) Vertical TFETs. This approach considers parameters such as contact potential (VGS, VDG), oxide thickness (tOX), and carrier mobility (µ). In the second approach, 2D Poisson s equations are solved to develop an analytical model for the proposed device. The channel s surface potential and electric field are modeled by solving 2-D Poisson s equation using the parabolic approximation approach.