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

Rajat Butola, Yiming Li, Sekhar Reddy Kola, Chieh Yang Chen, Min Hui Chuang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Microwave Theory and Techniques
DOIs
StateAccepted/In press - 2022

Keywords

  • Characteristic fluctuation
  • dc/analog/radio frequency (RF)
  • FinFETs
  • Gallium arsenide
  • gate-all-around (GAA) nanosheet field-effect transistors (NSFETs)
  • interface trap fluctuation (ITF)
  • intrinsic parameter fluctuation
  • Logic gates
  • machine learning (ML)
  • Nanoscale devices
  • Radio frequency
  • random dopant fluctuation (RDF)
  • Semiconductor process modeling
  • Silicon
  • work function fluctuation (WKF)

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