Accelerating Parameter Extraction of Power MOSFET Models Using Automatic Differentiation

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 article proposes an efficient model-parameter extraction method for compact models of power metal-oxide semiconductor field-effect transistors (
mosfet
s). 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.