Modeling and Simulation of MOSFET (High-k Dielectric) Using Genetic Algorithms

A Metal Oxide Semiconductor Field Effect Transistor (MOSFET) is a type of field effect transistor; the operation is based on the effect of an electric field applied to the metal-oxide-semiconductor structure, i.e., to the gate electrode. It is an essential electronic component, especially in the microelectronics industry. Since the invention of the integrated circuit, the principal growth vector of the silicon microelectronics industry has been the miniaturization of MOSFETs. To minimize the undesirable effects (for example leakage current) due to the miniaturization of MOSFETs, several solutions have been used to improve the performance of MOSFETs. Among these solutions is the use of new high permittivity gate oxides (for example, HfO2 oxide). A nanoscale MOSFET is a complex electronic device, and the simulation of nanoscale devices therefore needs new theories and modeling techniques (for example, artificial intelligence). The Genetic Algorithms (GAs) are adaptive metaheuristic algorithms based on the evolutionary ideas (Evolutionary Algorithms (EAs)) of natural selection and genetics. GAs are used to generate high-quality solutions to optimization and search problems by relying on genetic operators such as selection, crossover and mutation. This paper describes advanced modeling (optimization), simulation and parameter extraction of nanoscale MOSFETs (HfO2 oxide) using a GA approach. The electrical characteristics of MOSFETs are predicted according to different parameters (drain current, drain voltage, gate voltage, channel length, oxide thickness).