Estimating scalable common-denominator Laplace-domain MIMO models in an errors-in-variables framework

Design of electrical systems demands simulations using models evaluated in different design parameter choices. To enable the simulation of linear systems, one often requires their modeling as ordinary differential equations given tabular data obtained from device simulations or measurements. Existing techniques need to do this for every choice of design parameters since the model representations do not scale smoothly with the external parameter. The paper describes a frequency-domain identification algorithm to extract the poles and zeros of linear MIMO systems. Furthermore, it expresses the poles and zeros as trajectories that are functions of the design parameter(s). The paper describes the used framework, solves the starting-value problem, presents a solution for high-order systems and provides a model-order selection strategy. The properties of the algorithm are illustrated on microwave measurements of inductors, a variable gain amplifier and a high-order SAW-filter. As shown by these examples, the proposed identification algorithm is very well suited to derive scalable, physically relevant models out of tabular frequency response data.