Parametric macromodeling of lossy and dispersive multiconductor transmission lines

We propose an innovative parametric macromodeling technique for lossy and dispersive multiconductor transmission lines (MTLs) that can be used for interconnect modeling. It is based on a recently developed method for the analysis of lossy and dispersive MTLs extended by using the multivariate orthonormal vector fitting (MOVF) technique to build parametric macromodels in a rational form. They take into account design parameters, such as geometrical layout or substrate features, in addition to frequency. The presented technique is suited to generate state-space models and synthesize equivalent circuits, which can be easily embedded into conventional SPICE-like solvers. Parametric macromodels allow to perform design space exploration, design optimization, and sensitivity analysis efficiently. Numerical examples validate the proposed approach in both frequency and time domain

Non intrusive polynomial chaos-based stochastic macromodeling of multiport systems

We present a novel technique to efficiently perform the variability analysis of electromagnetic systems. The proposed method calculates a Polynomial Chaos-based macromodel of the system transfer function that includes its statistical properties. The combination of a non-intrusive Polynomial Chaos approach with the Vector Fitting algorithm allows to describe the system variability features with accuracy and efficiency. The results of the variability analysis performed with the proposed method are verified by means of comparison with respect to the standard Monte Carlo analysis

Time-domain parametric sensitivity analysis of multiconductor transmission lines

We present a new parametric macromodeling technique for lossy and dispersive multiconductor transmission lines (MTLs). This technique can handle design parameters, such as substrate or geometrical layout features, and provide time-domain sensitivity information for voltage and currents at the ports of the lines. It is based on a recently introduced spectral approach for the analysis of lossy and dispersive MTLs [1], [2] and it is suited to generate state-space models and synthesize equivalent circuits, which can be easily embedded into conventional SPICE-like solvers. Parametric macromodels which provide sensitivity information are well suited for design space exploration, design optimization and crosstalk analysis. A numerical example validates the proposed approach in both frequency and time domain

Stochastic macromodeling for hierarchical uncertainty quantification of nonlinear electronic systems

A hierarchical stochastic macromodeling approach is proposed for the efficient variability analysis of complex nonlinear electronic systems. A combination of the Transfer Function Trajectory and Polynomial Chaos methods is used to generate stochastic macromodels. In order to reduce the computational complexity of the model generation when the number of stochastic variables increases, a hierarchical system decomposition is used. Pertinent numerical results validate the proposed methodology

Parameterized model order reduction of delayed systems using an interpolation approach with amplitude and frequency scaling coefficients

When the geometric dimensions become electrically large or signal waveform rise times decrease, time delays must be included in the modeling. We present an innovative PMOR technique for neutral delayed differential systems, which is based on an efficient and reliable combination of univariate model order reduction methods, amplitude and frequency scaling coefficients and positive interpolation schemes. It is able to provide parameterized reduced order models passive by construction over the design space of interest. Pertinent numerical examples validate the proposed PMOR approach

Passivity-preserving parameterized model order reduction for PEEC based full wave analysis

We present a novel parameterized model order reduction technique applicable to the Partial Element Equivalent Circuit method that is able to generate parametric reduced order models, stable and passive by construction, over a user defined design space. Overall stability and passivity of the parametric reduced order model are guaranteed by an efficient and reliable combination of traditional passivity-preserving model order reduction methods and interpolation schemes based on a class of positive interpolation operators. A pertinent numerical example validates the proposed parameterized model order reduction approach

Polynomial chaos based variability analysis of multiport systems

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Microwave small-signal modelling of FinFETs using multi-parameter rational fitting method

An effective approach based on a multi-parameter rational fitting technique is proposed to model the microwave small-signal response of active solid-state devices. The model is identified by fitting multibias scattering-parameter measurements and its analytical expression is implemented in a commercial microwave circuit simulator. The approach has been applied to the modelling of a silicon-based FinFET, and an excellent agreement is obtained between the measured data and model predictions