Multi-objective genetic algorithms based approach to optimize the electrical performances of the gate stack double gate (GSDG) MOSFET

In this paper, a Multi-Objective Genetic Algorithm (MOGA)-based approach is proposed to study and optimize the electrical behavior of Gate Stack Double Gate (GSDG) MOSFET for deep submicron CMOS digital and analog circuit applications. The analytical models, which describe the electrical behavior, of the (GSDG) MOSFET such as OFF-current, threshold voltage roll-off, drain induced barrier lowering (DIBL), subthreshold swing and transconductance have been ascertained. The proposed compact models are used to formulate the objective functions, which are the pre-requisite of multi-objective genetic algorithms. The problem is then presented as a multi-objective optimization one where the subthreshold and saturation parameters are considered simultaneously. The proposed approach is used to find the optimal electrical and dimensional transistor parameters in order to obtain and explore the better transistor performances for analog and digital CMOS-based circuit applications.