Compact and passive parametric macromodeling using reference macromodels and positive interpolation operators

We present an enhanced parametric macromodeling method that is able to generate compact and passive models over the entire design space of interest. It starts from a discrete set of data samples of the input-output system behavior (e.g. admittance, impedance, and scattering parameters), which depend on multiple design variables such as layout and substrate parameters. The proposed approach generates accurate parametric macromodels whose size is not affected by the number of design parameters in addition to frequency. Stability and passivity are preserved over the design space of interest. Pertinent numerical results validate the proposed parametric macromodeling methods.We present an enhanced parametric macromodeling method that is able to generate compact and passive models over the entire design space of interest. It starts from a discrete set of data samples of the input-output system behavior (e.g. admittance, impedance, and scattering parameters), which depend on multiple design variables such as layout and substrate parameters. The proposed approach generates accurate parametric macromodels whose size is not affected by the number of design parameters in addition to frequency. Stability and passivity are preserved over the design space of interest. Pertinent numerical results validate the proposed parametric macromodeling methods.A