  The extraction of the model parameters is as important as the development of
compact model itself because simulation accuracy is fully determined by the
accuracy of the parameters 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 3.50x faster
than a conventional numerical-differentiation method while achieving the equal
accuracy.
