A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

Ya Shu Yang, Yiming Li, Sekhar Reddy Kola

Research output: Contribution to journalArticlepeer-review


We report a compact modeling framework based on the Grove&#x2013;Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs; the first ANN constructed with the drain current model not only can capture the main trend of device <inline-formula> <tex-math notation="LaTeX">$\textit{I}$</tex-math> </inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$\textit{V}$</tex-math> </inline-formula> characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalIEEE Transactions on Electron Devices
StateAccepted/In press - 2023


  • Circuit simulation
  • compact modeling framework
  • complementary FETs (CFETs)
  • emerging device modeling
  • machine learning (ML)
  • nanosheet (NS) FETs
  • neural network


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