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Enhancement-mode MOSFETs are pivotal components in electronics, distinguished by their capacity to act as highly efficient switches. They are part of the larger family of metal-oxide Semiconductor Field-Effect Transistors (MOSFETs). They are available in two types: p-channel and n-channel, each tailored to specific polarity operations.
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Interpreting artificial neural network-based modeling of 4 H-SiC mosfets using explainable AI.

Yu-Sheng Hsiao1, Pei-Jie Chang2, Bang-Ren Chen3

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This study introduces an explainable AI framework for modeling 4H-Silicon Carbide Metal-Oxide-Semiconductor Field-Effect Transistors (SiC MOSFETs). It accurately predicts device performance and interprets design impacts, enhancing power electronics development.

Keywords:
Device designExplainable AIMachine learningSiC mosfets

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Wide bandgap (WBG) semiconductors like 4H-SiC MOSFETs are crucial for advanced power electronics, offering high efficiency and thermal stability.
  • Traditional Technology Computer-Aided Design (TCAD) simulations for these devices are computationally intensive and lack scalability.
  • Process variations significantly impact the electrical performance of WBG semiconductors.

Purpose of the Study:

  • To develop a novel, explainable machine learning framework for accurate and interpretable device modeling of 4H-SiC MOSFETs.
  • To address the computational limitations and scalability issues of conventional TCAD simulations.
  • To provide a transparent, data-driven approach for understanding and optimizing semiconductor device design.

Main Methods:

  • Integration of artificial neural networks (ANNs) with explainable artificial intelligence (XAI) techniques, specifically SHapley Additive exPlanations (SHAP).
  • Training the ANN on extensive TCAD-generated datasets encompassing diverse structural and doping parameters.
  • Utilizing SHAP to quantify the influence of individual design parameters on device electrical characteristics.

Main Results:

  • The proposed model achieved a Pearson correlation coefficient greater than 0.99 for predicting on-state current.
  • SHAP analysis demonstrated physically consistent relationships, such as the inverse correlation between drain current and oxide thickness/channel length.
  • The framework successfully provided interpretable insights into design-performance correlations in SiC MOSFETs.

Conclusions:

  • This research establishes a transparent and data-driven framework for understanding and optimizing SiC MOSFETs using explainable AI.
  • The methodology offers a scalable and accurate alternative to conventional TCAD for device modeling.
  • The approach is adaptable to other semiconductor technologies requiring both high accuracy and interpretability in device modeling.