Deep Neural Network-based Lumped Circuit Modeling using Impedance Curve

Abstract

Usually, modeling takes a long time because it depends on the engineer's experience and is done through repetitive tuning. In this paper, we propose a deep neural network (DNN)-based lumped circuit modeling method using an impedance curve. The proposed method provides a fast and accurate electrical circuit model of inductance (L), capacitance (C), and conductance (G) using a DNN. Since the LCG parameters are predicted by the impedance curve, it is flexible for various applications. For accurately predicting lumped circuit parameters, the DNN model is designed and trained through various case studies. As a result, the proposed method predicts 100% accuracy in inductance and conductance, and 92% accuracy in capacitance. In other words, the proposed method successfully models the electrical characteristics of various applications.