Electrical Performance Optimization of Nanoscale Double-Gate MOSFETs Using Multiobjective Genetic Algorithms

Abstract

In this paper, a new multiobjective genetic algorithm (MOGA)-based approach is proposed to optimize the electrical performance of double-gate (DG) MOSFETs for nanoscale CMOS digital applications. The proposed approach combines the universal optimization and fitting capability of MOGAs and the cost-effective optimization concept of quantum correction to achieve reliable and optimized designs of DG MOSFETs for nanoelectronics analog and digital circuit simulations. The dimensional and electrical parameters of the DG MOSFET (threshold voltage rolloff, off-current, drain-induced barrier lowering, subthreshold swing ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> ), output conductance, and transconductance) have been ascertained, and a compact analytical expression, including quantum effects, has been presented. The developed compact models are used to formulate different objective functions, which are the prerequisite of the multiobjective optimization. The optimized design can also be incorporated into a circuit simulator to study and show the impact of our approach on a nanoscale CMOS-based circuit design.