Accelerating Parameter Extraction of Power MOSFET Models Using Automatic Differentiation

TL;DR

This paper introduces an automatic differentiation-based method for faster and equally accurate extraction of power MOSFET model parameters, significantly improving efficiency over traditional numerical methods.

Contribution

The study presents a novel AD-based parameter extraction technique that reduces computation time by 3.50 times without sacrificing accuracy.

Findings

Achieved 3.50x faster parameter extraction compared to conventional methods.

Successfully fitted current and capacitance characteristics of silicon carbide MOSFETs.

Maintained accuracy while significantly improving computational efficiency.

Abstract

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.