Michihiro Shintani, Aoi Ueda and Takashi Sato
Accelerating Parameter Extraction of Power MOSFET Models Using Automatic Differentiation
IEEE Transactions on Power Electronics (2021)
DOI: 10.1109/TPEL.2021.3118057
Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma (J)
Graduate School of Informatics, Kyoto University (J)
Abstract: The extraction of the model parameters is as im- portant as the development of compact model itself because simulation accuracy is fully determined by the accuracy of the pa- rameters used. This study proposes an efficient model-parameter extraction method for compact models of power MOSFETs. The proposed method employs automatic differentiation (AD), which is extensively used for training artificial neural networks. In the proposed AD-based parameter extraction, gradient of all the model parameters is analytically calculated by forming a graph that facilitates the backward propagation of errors. Based on the calculated gradient, computationally intensive numerical differentiation is eliminated and the model parameters are efficiently optimized. Experiments are conducted to fit current and capacitance characteristics of commercially available silicon carbide MOSFET using power MOSFET models having 13 model parameters. Results demonstrated that the proposed method could successfully derive the model parameters 3.50× faster than a conventional numerical-differentiation method while achieving the equal accuracy.
Fig: Backward propagation mode. The dashed arrows indicate the path from E to SCALE.
The propagated values on the path in the backward propagation are highlighted
Acknowledgment: This work was partially supported by JST-OPERA Program Grant Number JPMJOP1841, Japan. The part of this work is also supported by JSPS KAKENHI Grant 20H04156 and 20K21793.
