Artificial Neural Network based Modelling for Variational Effect on Double Metal Double Gate Negative Capacitance FET

In this work, we have implemented an accurate machine-learning approach for predicting various key analog and RF parameters of Negative Capacitance Field-Effect Transistors (NCFETs). Visual TCAD simulator and the Python high-level language were employed for the entire simulation process. However, the computational cost was found to be excessively high. The machine learning approach represents a novel method for predicting the effects of different sources on NCFETs while also reducing computational costs. The algorithm of an artificial neural network can effectively predict multi-input to single-output relationships and enhance existing techniques. The analog parameters of Double Metal Double Gate Negative Capacitance FETs (D2GNCFETs) are demonstrated across various temperatures ($T$), oxide thicknesses ($T_{ox}$), substrate thicknesses ($T_{sub}$), and ferroelectric thicknesses ($T_{Fe}$). Notably, at $T=300K$, the switching ratio is higher and the leakage current is $84$ times lower compared to $T=500K$. Similarly, at ferroelectric thicknesses $T_{Fe}=4nm$, the switching ratio improves by $5.4$ times compared to $T_{Fe}=8nm$. Furthermore, at substrate thicknesses $T_{sub}=3nm$, switching ratio increases by $81\%$ from $T_{sub}=7nm$. For oxide thicknesses at $T_{ox}=0.8nm$, the ratio increases by $41\%$ compared to $T_{ox}=0.4nm$. The analysis reveals that $T_{Fe}=4nm$, $T=300K$, $T_{ox}=0.8nm$, and $T_{sub}=3nm$ represent the optimal settings for D2GNCFETs, resulting in significantly improved performance. These findings can inform various applications in nanoelectronic devices and integrated circuit (IC) design.