This paper presents the first parameterized, SPICE-compatible compact model of a Graphene Nano-Ribbon Field-Effect Transistor (GNRFET) with doped reservoirs that also supports process variation. The current and charge models closely match numerical TCAD simulations. In addition, process variation in transistor dimension, edge roughness, and doping level in the reservoir are accurately modeled. Our model provides a means to analyze delay and power of graphene-based circuits under process variation, and offers design and fabrication insights for graphene circuits in the future. We show that edge roughness severely degrades the advantages of GNRFET circuits; however, GNRFET is still a good candidate for low-power applications.

The quest for developing graphene-based nanoelectronics puts new requirements on the science and technology of device modeling. It also heightens the role of device modeling in the exploration and in the early assessment of technology options. Graphene-based nanoelectronics is the first form of molecular electronics to reach real prominence, and therefore the role of single atoms and of chemical properties acquires more relevance than in the case of bulk semiconductors. In addition, the promising perspectives offered by band engineering of graphene through chemical modifications increase the role of quantum chemistry methods in the assessment of material properties. In this paper, we review the multiphysics multiscale (MS) approaches required to model graphene-based materials and devices, presenting a comprehensive overview of the main physical models providing a quantitative understanding of the operation of nanoscale transistors. We especially focus on the ongoing efforts to consistently connect simulations at different levels of physical abstraction in order to evaluate materials, device, and circuit properties. We discuss various attempts to induce a gap in graphene-based materials and their impact on the operation of different transistor structures. Finally, we compare candidate devices in terms of integrated circuit performance and robustness to process variability.

This paper describes a physics-based semi-analytical model for Schottky-barrier carbon nanotube (CNT) and graphene nanoribbon (GNR) transistors. The model includes the treatment of (i) both tunneling and thermionic currents, (ii) ambipolar conduction, i.e., both electron and hole current components, (iii) ballistic transport, and (iv) multi-band propagation. Further, it reduces the computational complexity in the two critical and time-consuming steps, namely the calculation of the tunneling probability and the self-consistent evaluation of the the surface potential in the channel. When validated against NanoTCAD ViDES, a quantum transport simulation framework based on the non-equilibrium Green's function method, it is several orders of magnitude faster without significant loss in accuracy. Since the model is physics-based, it is parameterizable and can be used to study the effect of common parametric variations in CNT diameter and GNR width, Schottky-barrier height, and insulator thickness.