Leveraging Machine Learning for Enhanced Design and Optimization of Gaussian‐Doped Trigate <scp>FinFETs</scp>

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.