A causal model for linear RF systems developed from frequency-domain measured data

With the ever-growing complexity of interconnect networks, models developed from measured data or data from 3-D electromagnetic simulators are increasingly becoming essential. It is to this end that the current contribution is directed. In particular, it focuses on the development of a model via a Fourier series expansion (FSE) approach. Its primary advantage is that the response in the time domain can be explicitly obtained in a simple form for an arbitrary input using only a set of FSE coefficients. Also, it guarantees causality without requiring a numerical implementation of a Hilbert transform. The end result is a causal and stable time-domain representation of a system that may subsequently be used in a time-domain simulator such as SPICE

Compact models for wireless systems

For the design and analysis of wireless systems, complex simulations are required and performed. Model order reduction techniques enable greater efficiencies to be achieved and concomitantly, a reduction in memory-resource usage. However, maintaining a certain level of accuracy is paramount. In this contribution, two techniques are combined to enable the formation of a compact model of a high-order system, structure or component. The first is a Krylov subspace method which reduces the original model to a moderate size and the second is a Fourier series expansion method that enables speed and ease of determination of the time-domain responses of the system to arbitrary inputs

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

A parametric macromodelling technique

With the ever growing complexity of high-frequency systems in the electronic industry, formation of reduced-order models or compact macromodels of these systems is paramount. In this contribution, a Fourier series expansion technique is extended to form a modeling strategy to approximate the frequency-domain behaviour of a system based on several design variables. In particular, it is intended to provide a tool for the designer to identify the effect of manufacturer tolerances and process fluctuations or irregularities on system behaviour

An explicit mapping between the frequency domain and the time domain representations of nonlinear systems

Explicit expressions are presented that describe the input-output behaviour of a nonlinear system in both the frequency and the time domain. The expressions are based on a set of coefficients that do not depend on the input to the system and are universal for a given system. The anharmonic oscillator is chosen as an example and is discussed for different choices of its physical parameters. It is shown that the typical approach for the determination of the Volterra Series representation is not valid for the important case when the nonlinear system exhibits oscillatory behaviour and the input has a pole at the origin (in the frequency domain), e.g. the unit-step function. For this case, resonant effects arise and the analysis requires additional care.Comment: 27 pages, 5 figures, .pd