Abstract
To enable fast and accurate models of SiC MOSFETs for transient simulation, a hybrid data-driven modeling methodology of SiC MOSFETs is proposed. Unlike conventional modeling methods that are based on complex nonlinear equations, data-driven artificial neural networks (ANNs) are used in this article. For model accuracy, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$I$</tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V$</tex-math></inline-formula> characteristics are measured in the whole operation region to train the ANN. The ANN model is then combined with behavior-based equations to model the cutoff region and to avoid overfitting the ANN. In addition, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C$</tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V$</tex-math></inline-formula> characteristics are modeled by ANNs with a logarithmic scale for accuracy. The proposed model is implemented and simulated in SPICE simulator SIMetrix. The simulation results are compared with experimental results from a double pulse tester to validate the proposed modeling methodology. The model is also compared with the Angelov model created by the Keysight MOSFET modeling software. The comparison results show that the proposed model is more accurate than the Angelov model. Besides, when compared to the Angelov model, the proposed model requires 30% less computation time when simulating a double pulse tester. In addition, the proposed modeling method also has better adaptability to model different types of SiC MOSFETs.
Highlights
S ILICON Carbide (SiC) metal-oxide-semiconductor fieldeffect-transistors (MOSFETs) are gaining popularity in recent years
The proposed method improves accuracy of SiC MOSFET models by 1.5 ∼ 3 times, compared to the widely used Angelov model
The accuracy of the model is largely determined by two factors, i.e., high-quality training dataset and proper data-driven model, which are detailed as follows: 1) For the training dataset of the I-V characteristics, compared to existing methods [23], it is identified that the I-V characteristics in the high voltage region must be included to obtain accurate simulation results across the whole operation region of SiC MOSFETs in transient simulation
Summary
S ILICON Carbide (SiC) metal-oxide-semiconductor fieldeffect-transistors (MOSFETs) are gaining popularity in recent years. Compared to the experimental approach, the computer-aided-design methodology is more time-efficient, as it can be used to simulate the power losses and EMI for converter optimization This approach requires accurate and fast SiC MOSFET model to predict the switching waveforms to nanosecond level accuracy. The accuracy of the model is largely determined by two factors, i.e., high-quality training dataset and proper data-driven model, which are detailed as follows: 1) For the training dataset of the I-V characteristics, compared to existing methods [23], it is identified that the I-V characteristics in the high voltage region (i.e. saturation region up to the maximum DC voltage across the MOSFETs) must be included to obtain accurate simulation results across the whole operation region of SiC MOSFETs in transient simulation. The proposed modeling method improves the accuracy of transient simulation, which in turn can be used for accurate analysis of power losses and EMI in computeraided design of power converters based on SiC MOSFETs
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