Single-Input-Single-Output Passive Macromodeling via Positive Fractions Vector Fitting

This paper introduces a constrained vector fitting algorithm which can directly identify a passive driving point function (impedance or admittance) from frequency domain data. The proposed positive fractions vector fitting (PFVF) algorithm formulates the residue identification step as a convex programming problem, while the pole identification step follows the unaltered standard Vector Fitting procedure. A further extension to multi-input-multi-output functions is possible and is under investigation.