Delay extraction-based passive macromodeling techniques for transmission line type interconnects characterized by tabulated multiport data

Charest, Andrew; Achar, R.; Nakhla, M.; Erdin, Ihsan

doi:10.1007/s10470-008-9201-x

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…(16c) defined using the partial fractions basis for for (17) The adopted transfer function of (15a)-(15d) mimics the method of characteristics model for a uniform transmission line. It includes a combination of delay operators to represent pure propagation, and rational terms to represent nonideal effects like signal dispersion and attenuation.…”

Section: B Structure Of Modal Macromodelsmentioning

confidence: 99%

Identification of Highly Efficient Delay-Rational Macromodels of Long Interconnects From Tabulated Frequency Data

Triverio

Grivet-Talocia

Chinea

2010

IEEE Trans. Microwave Theory Techn.

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We introduce a novel formulation of black-box models for long multiconductor interconnects, together with an identification algorithm from tabulated scattering parameters. The fundamental assumption requires a modal decomposition matrix that does not depend on frequency. The model structure includes loworder rational coefficients with suitable delay operators. The latter are included in a feedback loop; therefore, all infinite signal reflections are automatically accounted for. This feature may lead to significant speed up factors during circuit-based transient simulation with respect to other state-of-the-art solutions. The model is cast in a delayed descriptor form, leading to a straightforward conversion to an equivalent SPICE-compatible netlist. Finally, a purely algebraic stability test is presented based on a linear matrix inequality. The very high efficiency of the proposed models is demonstrated through several application examples.

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Section: B Structure Of Modal Macromodelsmentioning

confidence: 99%

Identification of Highly Efficient Delay-Rational Macromodels of Long Interconnects From Tabulated Frequency Data

Triverio

Grivet-Talocia

Chinea

2010

IEEE Trans. Microwave Theory Techn.

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“…These models are often obtained either from direct measurements or electromagnetic simulations in the form of discrete port frequency responses that represent scattering, impedance, or admittance transfer functions or transfer matrices in scalar or multidimensional cases, respectively. Once frequency responses are available, a corresponding macromodel can be derived using several techniques such as the Vector Fitting [2], the Orthonormal Vector Fitting [3], the Delay ExtractionBased Passive Macromodeling [4] among others. However, if the data are contaminated by errors, it may not be possible to derive a good model.…”

Section: Introductionmentioning

confidence: 99%

Spectrally Accurate Causality Enforcement Using SVD-Based Fourier Continuations for High-Speed Digital Interconnects

Barannyk

Aboutaleb²,

Elshabini

et al. 2015

IEEE Trans. Compon., Packag. Manufact. Technol.

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Abstract-We introduce an accurate and robust technique for accessing causality of network transfer functions given in the form of bandlimited discrete frequency responses. These transfer functions are commonly used to represent the electrical response of high speed digital interconnects used on chip and in electronic package assemblies. In some cases small errors in the model development lead to non-causal behavior that does not accurately represent the electrical response and may lead to a lack of convergence in simulations that utilize these models. The approach is based on Hilbert transform relations or KramersKrönig dispersion relations and a construction of causal Fourier continuations using a regularized singular value decomposition (SVD) method. Given a transfer function, non-periodic in general, this procedure constructs highly accurate Fourier series approximations on the given frequency interval by allowing the function to be periodic in an extended domain. The causality dispersion relations are enforced spectrally and exactly. This eliminates the necessity of approximating the transfer function behavior at infinity and explicit computation of the Hilbert transform. We perform the error analysis of the method and take into account a possible presence of a noise or approximation errors in data. The developed error estimates can be used in verifying causality of the given data. The performance of the method is tested on several analytic and simulated examples that demonstrate an excellent accuracy and reliability of the proposed technique in agreement with the obtained error estimates. The method is capable of detecting very small localized causality violations with amplitudes close to the machine precision.

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“…or electromagnetic simulations in the form of discrete port frequency responses that represent scattering, impedance, or admittance transfer functions or transfer matrices in scalar or multidimensional cases, respectively. Once frequency responses are available, a corresponding macromodel can be derived using several techniques such as the Vector Fitting [2], the Orthonormal Vector Fitting [3], the Delay Extraction-Based Passive Macromodeling [4] among others. However, if the data are contaminated by errors, it may not be possible to derive a good model.…”

Section: Introductionmentioning

confidence: 99%

Spectrally Accurate Causality Enforcement using SVD-based Fourier Continuations for High Speed Digital Interconnects

Barannyk¹,

Aboutaleb²,

Elshabini³

et al. 2014

Preprint

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We introduce an accurate and robust technique for accessing causality of network transfer functions given in the form of bandlimited discrete frequency responses. These transfer functions are commonly used to represent the electrical response of high speed digital interconnects used on chip and in electronic package assemblies. In some cases small errors in the model development lead to non-causal behavior that does not accurately represent the electrical response and may lead to a lack of convergence in simulations that utilize these models. The approach is based on Hilbert transform relations or Kramers-Krönig dispersion relations and a construction of causal Fourier continuations using a regularized singular value decomposition (SVD) method. Given a transfer function, non-periodic in general, this procedure constructs highly accurate Fourier series approximations on the given frequency interval by allowing the function to be periodic in an extended domain. The causality dispersion relations are enforced spectrally and exactly. This eliminates the necessity of approximating the transfer function behavior at infinity and explicit computation of the Hilbert transform. We perform the error analysis of the method and take into account a possible presence of a noise or approximation errors in data. The developed error estimates can be used in verifying causality of the given data. The performance of the method is tested on several analytic and simulated examples that demonstrate an excellent accuracy and reliability of the proposed technique in agreement with the obtained error estimates. The method is capable of detecting very small localized causality violations with amplitudes close to the machine precision.

show abstract

Delay extraction-based passive macromodeling techniques for transmission line type interconnects characterized by tabulated multiport data

Cited by 5 publications

References 14 publications

Identification of Highly Efficient Delay-Rational Macromodels of Long Interconnects From Tabulated Frequency Data

Identification of Highly Efficient Delay-Rational Macromodels of Long Interconnects From Tabulated Frequency Data

Spectrally Accurate Causality Enforcement Using SVD-Based Fourier Continuations for High-Speed Digital Interconnects

Spectrally Accurate Causality Enforcement using SVD-based Fourier Continuations for High Speed Digital Interconnects

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