Leveraging Machine Learning for Enhanced Design and Optimization of Gaussian-Doped Trigate FinFETs

Abstract

Fin-shaped Field Effect Transistors (FinFETs) are essential in the world of sub-nanometer technology nodes because of their remarkable scalability and electrostatic control. This work presents a new, optimized, and small-scale Gaussian-doped FinFET design that improves analog performance and minimizes short channel effects over conventional planar MOSFETs. Our unique structure leverages an Artificial Neural Network (ANN) in conjunction with a Genetic Algorithm (GA) for optimization. The dataset for ANN training was meticulously generated by designing and simulating Gaussian-doped FinFETs with varying Fin-width (W_Fin) and Fin-height (H_Fin). Through this process, we identified optimal W_Fin and H_Fin values that significantly improve performance characteristics. The optimized Gaussian-doped FinFET demonstrates superior control over short channel effects, as evidenced by a subthreshold swing (SS) of 66 mV/dec, an off-state current (I_OFF) of 3.54 pA, and an on-state current (I_ON) of 12 μA. The close alignment between the optimized and simulated performance characteristics, with less than a 5% variance, underscores the efficacy of our optimization approach.