Guaranteed passive parameterized model order reduction of the partial element equivalent circuit (PEEC) method

The decrease of IC feature size and the increase of operating frequencies require 3-D electromagnetic methods, such as the partial element equivalent circuit (PEEC) method, for the analysis and design of high-speed circuits. Very large systems of equations are often produced by 3-D electromagnetic methods. During the circuit synthesis of large-scale digital or analog applications, it is important to predict the response of the system under study as a function of design parameters, such as geometrical and substrate features, in addition to frequency (or time). Parameterized model order reduction (PMOR) methods become necessary to reduce large systems of equations with respect to frequency and other design parameters. We propose an innovative PMOR technique applicable to PEEC analysis, which combines traditional passivity-preserving model order reduction methods and positive interpolation schemes. It is able to provide parametric reduced-order models, stable, and passive by construction over a user-defined range of design parameter values. Numerical examples validate the proposed 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

Structure-Preserving Model Reduction of Physical Network Systems

This paper considers physical network systems where the energy storage is naturally associated to the nodes of the graph, while the edges of the graph correspond to static couplings. The first sections deal with the linear case, covering examples such as mass-damper and hydraulic systems, which have a structure that is similar to symmetric consensus dynamics. The last section is concerned with a specific class of nonlinear physical network systems; namely detailed-balanced chemical reaction networks governed by mass action kinetics. In both cases, linear and nonlinear, the structure of the dynamics is similar, and is based on a weighted Laplacian matrix, together with an energy function capturing the energy storage at the nodes. We discuss two methods for structure-preserving model reduction. The first one is clustering; aggregating the nodes of the underlying graph to obtain a reduced graph. The second approach is based on neglecting the energy storage at some of the nodes, and subsequently eliminating those nodes (called Kron reduction).</p

Towards fully automated high-dimensional parameterized macromodeling

This paper presents a fully automated algorithm for the extraction of parameterized macromodels from frequency responses. The reference framework is based on a frequency-domain rational approximation combined with a parameter-space expansion into Gaussian Radial Basis Functions (RBF). An iterative least-squares fitting with positivity constraints is used to optimize model coefficients, with a guarantee of uniform stability over the parameter space. The main novel contribution of this work is a set of algorithms, supported by strong theoretical arguments with associated proofs, for the automated determination of all the hyper-parameters that define model orders, placement and width of RBFs. With respect to standard approaches, which tune these parameters using time-consuming tentative model extractions following a trial-and-error strategy, the presented technique allows much faster tuning of the model structure. The numerical results show that models with up to ten independent parameters are easily extracted in few minutes

A Novel Framework for Parametric Loewner Matrix Interpolation

The generation of black-box macromodels of passive components at the chip, package, and board levels has become an important step of the electronic design automation (EDA) workflow. The vector fitting (VF) scheme is a very popular method for the extraction of such macromodels, and several multivariate extensions are now available for embedding external parameters in the model structure, thus enabling model-based variability analysis and design optimization. The Loewner matrix interpolation framework was recently suggested as an effective and promising alternative macromodeling approach to VF. In this article, we propose a parametric version of Loewner interpolation, which embeds orthogonal polynomials as an integral part of the parameterization framework. This approach is shown to be efficient and accurate and presents various advantages with respect to competing multivariate rational interpolation methods. These advantages include better control of model smoothness in the parameter space and a particularly efficient implementation of the singular value decomposition, which is the core of the model extraction scheme. These advantages are confirmed through several examples relevant for signal and power integrity applications

Constrained extremal problems in H2 and Carleman's formulas

International audienceWe consider the extremal problem of best approximation to some function $f$ in $L^2(I)$, with $I$ a subset of the circle, by the trace of a Hardy function whose modulus is bounded pointwise by some gauge function on the complementary subset

Disturbance attenuation in linear systems revisited

A variety of methods have been proposed for attenuating or rejecting disturbances in linear systems. Most of the approaches, however, are targeting the performance either near resonance or across the whole frequency range. Weighting functions can be utilized to shape the frequency response function over target frequency band but they are usually of rule-of-thumb nature. A methodology is necessitated for designing controllers with performance specified at any discrete frequency or over any desired frequency band. The paper aims to develop such a methodology. Besides this, the proposed method can tell performance limitations and determine the problem of existence of optimal controllers, as well as providing a useful framework to improve the performance of an existing controller. A number of important results are obtained and these results are subsequently validated through a practical application to a rotor blade example

Multipoint Schur's algorithm, rational orthogonal functions, asymptotic properties and Schur rational approximation

In a paper by Khrushchev, the connections between the Schur algorithm, the Wall's continued fractions and the orthogonal polynomials are revisited and used to establish some nice convergence properties of the sequence of Schur functions associated with a Schur function. In this report, we generalize some of Krushchev's results to the case of a multipoint Schur algorithm, that is a Schur algorithm where all the interpolation points are not taken in 0 but anywhere in the open unit disk. To this end, orthogonal rational functions and a recent generalization of Geronimus theorem are used. Then, we consider the problem of approximating a Schur function by a rational function which is also Schur. This problem of approximation is very important for the synthesis and identification of passive systems. We prove that all strictly Schur rational function of degree $n$ can be written as the $2n$-th convergent of the Schur algorithm if the interpolation points are correctly chosen. This leads to a parametrization using the multipoint Schur algorithm. Some examples are computed by an $L^2$ norm optimization process and the results are validated by comparison with the unconstrained $L^2$ rational approximation

Modeling Systems from Measurements of their Frequency Response

The problem of modeling systems from frequency response measurements is of interest to many engineers. In electronics, we wish to construct a macromodel from tabulated impedance, admittance or scattering parameters to incorporate it into a circuit simulator for performing circuit analyses. Structural engineers employ frequency response functions to determine the natural frequencies and damping coefficients of the underlying structure. Subspace identification, popular among control engineers, and vector fitting, used by electronics engineers, are examples of algorithms developed for this problem. This thesis has three goals. 1. For multi-port devices, currently available algorithms arc expensive. This thesis therefore proposes an approach based on the Loewner matrix pencil constructed in the context of tangential interpolation with several possible implementations. They are fast, accurate, build low dimensional models, and are especially designed for a large number of terminals. For noise-free data, they identify the underlying system, rather than merely fitting the measurements. For noisy data, their performance is analyzed for different noise levels introduced in the measurements and an improved version, which identifies an approximation of the original system even for large noise values, is proposed. 2. This thesis addresses the problem of generating parametric models from measurements performed with respect to the frequency, but also with respect to one or more design parameters, which could relate to geometry or material properties. These models are suited for performing optimization over the design variables. The proposed approach generalizes the Loewner matrix to data depending on two variables. 3. This thesis analyzes the convergence properties of vector fitting, an iterative algorithm that relocates the poles of the model, given some "starting poles" chosen heuristically. It was recognized as a reformulation of the Sanathanan-Koerner iteration and several authors attempted to improve its convergence properties, but a thorough convergence analysis has been missing. Numerical examples show that for high signal to noise ratios, the iteration is convergent, while for low ones, it may diverge. Hence, incorporating a Newton step aims at making the iteration always convergent for "starting poles" chosen close to the solution. A connection between vector fitting and the Loewner framework is exhibited, which resolves the issue of choosing the starting poles