Artificial Neural Network-Based Modeling for Estimating the Effects of Various Random Fluctuations on DC/Analog/RF Characteristics of GAA Si Nanosheet FETs

Abstract

Advanced field-effect transistors (FETs), such as gate-all-around (GAA) nanowire (NW) and nanosheet (NS) devices, have been highly scaled; therefore, they are critically affected even by a microscopic fluctuation. As the GAA NS device is considered a promising candidate beyond 5-nm technology, it is essential to analyze the effects of these fluctuations on dc and analog/radio frequency (RF) characteristics for future applications. In this article, we for the first time demonstrate that the machine learning (ML)-aided numerical device simulation approach can be used to model the effects of various fluctuations on the characteristics of GAA NS FETs (NSFETs). Among various fluctuations, we mainly focus on work function fluctuation (WKF), random dopant fluctuation (RDF), and interface trap fluctuation (ITF). The independent and combined effects of these fluctuations on the characteristics of NSFETs are studied. Except for transfer and output characteristics, analog and RF parameters, such as gate capacitance, transconductance, cutoff frequency, 3-dB frequency, and transconductance efficiency, are analyzed in detail. The main aim of this work is to show the capability and generality of ML in modeling various electrical characteristics of the explored NSFETs. The results show that the ML-based technique is fast and efficient, which accelerates the overall process and gives engineering acceptable accurate results.