Deep-Learning-Assisted Physics-Driven MOSFET Current-Voltage Modeling

Ming Yen Kao*, H. Kam, Chenming Hu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this work, we propose using deep learning to improve the accuracy of the partially-physics-based conventional MOSFET current-voltage model. The benefits of having some physics-driven features in the model are discussed. Using a portion of the Berkeley Short-channel IGFET Common-Multi-Gate (BSIM-CMG), the industry-standard FinFET and GAAFET compact model, as the physics model and a 3-layer neural network with 6 neurons per layer, the resultant model can well predict IV, output conductance, and transconductance of a TCAD-simulated gate-all-around transistor (GAAFET) with outstanding 3-sigma errors of 1.3%, 4.1%, and 2.9%, respectively. Implications for circuit simulation are also discussed.

Original languageEnglish
Pages (from-to)974-977
Number of pages4
JournalIeee Electron Device Letters
Volume43
Issue number6
DOIs
StatePublished - 1 Jun 2022

Keywords

  • BSIM-CMG
  • compact model
  • deep learning
  • MOSFET

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