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

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.