Marine predators algorithm for performance optimization of nanoscale FinFET

Abstract

Fin-shaped Field Effect Transistors (FinFETs) have proved to be an effective solution when transistors are downscaled and conventional MOSFETs do not perform appreciably well in nanoscale regime. The current work emphasizes on the optimization of Rectzoidal FinFET design at shorter gate length and evaluates short channel effects for 20 nm gate length. Various device dimensions like fin width, fin height, equivalent gate oxide thickness, gate material work-function decide the device performance which is, in fact, measured by leakage current, on-current and short channel effects such as Subthreshold swing. Design and simulations for device have been executed in TCAD tool. After simulations, the performance of designed FinFET structure is optimized with the use of marine predators algorithm (MPA), which is an effective optimization technique inspired from foraging strategy opted by ocean predators. A dataset of fin height and fin width acts as input to artificial neural network, which outputs a trained network. Trained network along with the fitness function is inputted to MPA, and optimized output when verified with TCAD results, produces acceptable results. Fitness function tends to reduce the short channel effects and enhance on-current of device. Approximate optimized fin width and fin height values of designed FinFET are 5 nm and 24 nm respectively. This optimized miniaturized device design is appropriate for transistors which are further employed in numerous nanoscale applications.