Design and simulation of a nanoelectronic DG MOSFET current source using artificial neural networks

Abstract

The double gate (DG) MOSFET has received great attention in recent years owing to the inherent suppression of short channel effects (SCEs), excellent subthreshold slope (S), improved drive current (Ids) and transconductance (gm), volume inversion for symmetric devices and excellent scalability. Therefore, simulation tools which can be applied to design nanoscale transistors in the future require new theory and modeling techniques that capture the physics of quantum transport accurately and efficiently. In this sense, this work presents the applicability of the artificial neural networks (ANN) for the design and simulation of a nanoelectronic DG MOSFET current source. The latter is based on the 2D numerical Non-Equilibrium Green's Function (NEGF) simulation of the current–voltage characteristics of an undoped symmetric DG MOSFET. Our results are discussed in order to obtain some new and useful information about the ULSI technology.