Jan De Geest

Adaptive Sampling Algorithm for Accurate Modeling of General Interconnection Structures

A new adaptive technique is presented for building multidimensional parameterized analytical models for planar interconnection structures with a pre-defined accuracy and based on full-wave electromagnetic (EM) simulations. The models can be incorporated in a circuit simulator and the time required to calculate the circuit representation of a practical network is reduced by several orders of magnitude compared to full EM simulations. Furthermore, the accuracy of the results is significantly better compared to the results obtained with the circuit models used in state of the art CAD tools.

A Generative Modeling Framework for Statistical Link Analysis Based on Sparse Data

This paper proposes a novel strategy for creating generative models of stochastic link responses starting from limited available data. Whereas state-of-the-art techniques, e.g., based on generalized polynomial chaos expansions, require a considerable amount of (expensive) input data, here we start from a small set of “training” responses. These responses are obtained either from simulations or measurements to construct a comprehensive stochastic model. Using this model, new response samples can be generated with a distribution as similar as possible to the real data distribution, for use in Monte Carlo-like analyses. The methodology first uses the standard Vector Fitting algorithm to fit the S-parameter data with rational functions having common poles. Then, a generative model for the residues is created by means of principal component analysis and kernel density estimation. An a posteriori selection of passive samples is performed on the generated data to ensure the new samples are physically consistent. The proposed modeling approach is applied to a commercial connector and to a set of differential striplines. Both are concatenated to produce the stochastic analysis of a complete link. Comparisons on the prediction of time-domain responses are also provided.

A novel methodology to create generative statistical models of interconnects

This paper addresses the problem of constructing a generative statistical model for an interconnect starting from a limited set of S-parameter samples, which are obtained by simulating or measuring the interconnect for a few random realizations of its stochastic physical properties. These original samples are first converted into a pole-residue representation with common poles. The corresponding residues are modeled as a correlated stochastic process by means of principal component analysis and kernel density estimation. The obtained model allows generating new samples with similar statistics as the original data. A passivity check is performed over the generated samples to retain only passive data. The proposed approach is applied to a representative coupled microstrip line example.

Self-organizing multivariate constrained meta-modeling technique for passive microwave and RF components

A self-organizing algorithm is developed for multivariate constrained modeling of general passive components. The algorithm builds compact, analytical circuit models and represents the scattering parameters of the passive components as a function of its geometrical parameters and as a function of the frequency. Multiple constraints, or relationships between the geometrical parameters, may exist. The model generation algorithm combines iterative sampling and modeling techniques. It groups a number of full-wave electromagnetic simulations in one multivariate analytic model. The modeling accuracy level is user-defined. The analytical circuit models can easily be implemented and used in commercial circuit simulators. The model extraction provides EM-accuracy and generality at traditional circuit simulation speed.

Parameterized Circuit Modeling of Planar Transmission Line Structures on Arbitrary Substrates

A new automated circuit-modeling tool is presented for arbitrary planar transmission lines. The tool builds compact, parameterized, analytical models based on multiple full-wave 2D electro-magnetic (EM) simulations. The transmission line parameters are stored as a multidimensional function of frequency and geometrical parameters. The modeling algorithm combines adaptive data selecting and modeling techniques. The circuit models combine EM-accuracy and generality, and circuit simulation speed and flexibility.