Details

Autor(en) / Beteiligte

• Yang, Ya-Shu
• Li, Yiming
• Kola, Sekhar Reddy

Titel

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

Ist Teil von

• IEEE transactions on electron devices, 2024-01, Vol.71 (1), p.1-8

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2024

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore Digital Library

Beschreibungen/Notizen

• We report a compact modeling framework based on the Grove-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>-<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.