Energy Effective Graphene Based Computing
In this presentation we argue and provide Non-Equilibrium Green’s Function Landauer formalism-based simulation evidence that in spite of Graphene’s bandgap absence, Graphene Nanoribbons (GNRs) can provide support for energy effective computing. We start by demonstrating that: (i) band gap can be opened by means of GNR topology and (ii) GNR’s conductance can be mold according to some desired functionality, i.e., 2- and 3-input AND, NAND, OR, NOR, XOR, and XNOR, via shape and electrostatic interaction. Afterwards, we introduce a generic GNR based Boolean gate structure composed of a pull-up GNR performing the gate Boolean function and a pull-down GNR performing the gate inverted Boolean function, and, by properly adjusting GNRs' dimensions and topology, we design and evaluate by means of SPICE simulations inverter, buffer, and 2-input GNR based AND, NAND, and XOR gates. Finally, we compare the proposed gates with state-of-the-art graphene FET and CMOS based counterparts. Our analysis suggests that the GNR-based gates outperform its challengers, e.g., 2019-2021 CASS Distinguished Lecturer Roster up to 6x smaller propagation delay, 2 orders of magnitude smaller power consumption, while requiring 1 to 2 orders of magnitude smaller active area footprint when compared with 7nm CMOS equivalents, which is a clear indication that they have great potential as basic building blocks for future beyond CMOS energy effective nanoscale circuits.