Efficient and Accurate Neural Network-Based Macro-models for Spiral Inductors

Abstract

A neural network approach is presented for efficient and accurate model parameter extraction for integrated circuit spiral inductors. The approach involves the creation of neural network models which use spiral inductor geometric characteristics for prediction of s- and y-parameters over the frequency range 0.1GHz - 100GHz. The objective is to develop an efficient replacement neural model for spiral inductors - a circuit element for which a detailed simulation model already exists. The approach is especially attractive because it is capable of modeling skin and proximity effects as well as changes in effective series resistance at higher frequencies. Additionally, the neural network macro-model can predict important inductor characteristics such as Q-factor and self-resonant frequency. Substantial computational savings over detailed circuit simulation are available - ranging from 88 % to 95%.