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

Abstract

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.