Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Matrix computations (3rd ed.)
Introduction to total least squares techniques and errors-in-variables modeling
Proceedings of the second international workshop on Recent advances in total least squares techniques and errors-in-variables modeling
Robust rational function approximation algorithm for model generation
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
A Gauss-Newton-like optimization algorithm for“weighted” nonlinear least-squares problems
IEEE Transactions on Signal Processing
Automatica (Journal of IFAC)
PRIMA: passive reduced-order interconnect macromodeling algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Efficient linear circuit analysis by Pade approximation via the Lanczos process
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Design of electrical systems demands simulations using models evaluated in different design parameters 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 dont 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.