Causal and stable reduced-order model for linear high-frequency systems

With the ever-growing complexity of high-frequency systems in the electronic industry, formation of reduced-order models of these systems is paramount. In this reported work, two different techniques are combined to generate a stable and causal representation of the system. In particular, balanced truncation is combined with a Fourier series expansion approach. The efficacy of the proposed combined method is shown with an example

Combining Krylov subspace methods and identification-based methods for model order reduction

Many different techniques to reduce the dimensions of a model have been proposed in the near past. Krylov subspace methods are relatively cheap, but generate non-optimal models. In this paper a combination of Krylov subspace methods and orthonormal vector fitting (OVF) is proposed. In that way a compact model for a large model can be generated. In the first step, a Krylov subspace method reduces the large model to a model of medium size, then a compact model is derived with OVF as a second step

Adaptive classification algorithm for EMC-compliance testing of electronic devices

A novel technique that facilitates near-field (NF) scanning for electromagnetic compatibility-compliance testing is described. It performs measurements in a sequential way with the aim of discovering multiple, possibly disjoint regions where the amplitudes of an NF component belong to certain output ranges. The measured data samples are used to train a classification model where each NF range is represented by a given class (e.g. low/medium/high NF amplitudes). The outcome of the algorithm is a visual map that clearly characterises and pinpoints the exact location and boundaries of each class. Such maps are useful, for example, to detect hotspots or regions that are prone to electromagnetic compatibility issues. The technique has been validated on a measured microstrip bend discontinuity

On tuning passive black-box macromodels of LTI systems via adaptive weighting

This paper discusses various approaches for tuning the accuracy of rational macromodels obtained via black-box identification or approximation of sampled frequency responses of some unknown Linear and Time-Invariant system. Main emphasis is on embedding into the model extraction process some information on the nominal terminations that will be connected to the model during normal operation, so that the corresponding accuracy is optimized. This goal is achieved through an optimization based on a suitably defined cost function, which embeds frequency-dependent weights that are adaptively refined during the model construction. A similar procedure is applied in a postprocessing step for enforcing model passivity. The advantages of proposed algorithm are illustrated on a few application examples related to power distribution networks in electronic system

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