On the construction of guaranteed passive macromodels for high-speed channels

Chinea,; Grivet-Talocia,; Deschrijver,; Dhaene,; Knockaert,

doi:10.1109/date.2010.5456980

Cited by 8 publications

(10 citation statements)

References 13 publications

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“…, L can be performed via the Delayed Vector Fitting (DVF) or the Delayed Sanathanan-Koerner (DSK) iterations, see [12]. Model passivity can also be checked end enforced [14]- [17].…”

Section: Delay-rational Macromodelsmentioning

confidence: 99%

“…The former aim at representing attenuation and dispersion effects, while the latter are responsible for a physicsconsistent representation of the finite-speed propagation along the path, including the effects of multiple discontinuities causing signal reflections. DRM's can be efficiently identified using black-box fitting algorithms from tabulated frequency samples, and several methods are available for checking and enforcing their passivity [14]- [17].…”

Section: Introductionmentioning

confidence: 99%

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A two-level waveform relaxation approach for fast transient simulation of long high-speed interconnects

Loggia

Grivet-Talocia

2010

19th Topical Meeting on Electrical Performance of Electronic Packaging and Systems

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This paper presents a waveform relaxation approach for efficient transient simulation of fully-coupled high-speed channels of medium to large electrical length. The channel is first described via a passive Delayed Rational Macromodel, which is suitably identified from tabulated scattering data. The structure of the macromodel is then exploited to setup a two-level iterative relaxation scheme for the solution of terminal voltage and current waveforms. Numerical results show that the proposed scheme outperforms standard SPICE-based solvers at no loss of accuracy.

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“…, L can be performed via the Delayed Vector Fitting (DVF) or the Delayed Sanathanan-Koerner (DSK) iterations, see [12]. Model passivity can also be checked end enforced [14]- [17].…”

Section: Delay-rational Macromodelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A two-level waveform relaxation approach for fast transient simulation of long high-speed interconnects

Loggia

Grivet-Talocia

2010

19th Topical Meeting on Electrical Performance of Electronic Packaging and Systems

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“…The results also showed that the QR technique didn't predict the number of po Without knowing the correct number of pole check will not be accurate and the end user to ensure the validity of the check. Un technique that is able to accurately predict poles, therefore, ensure the validity of the ch V. PASSIVITY IDENTIFICATIO Passivity checking has to take place af rational function by the existing and pro There are multiple techniques available checking [16][17][18][19]. In this paper the checking the Hamiltonian method to verify the ac provided solution.…”

Section: Numerical Examplementioning

confidence: 98%

Passivity Verification and Macromodel Interpolation Using Singular Value Decomposition (SVD)

Elgamel

Greeff

Ovard

2015

2015 IEEE Workshop on Microelectronics and Electron Devices (WMED)

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Approximation of the Rational Function (RF) order plays a key role in passivity checking of large interconnect memory design. RF approximation can be estimated using the Least-square solutions; the Singular Value Decomposition (SVD) is used to solve large order macromodels. The interpolation method is used to solve linear systems by approximating the RF degree and coefficients. However, building the macromodel requires RF order approximation, which can lead to inaccurate passivity checking for complex systems. While inaccurate order estimation may lead to inaccuracy in the passivity checking process and drive to unnecessary passivity enforcement to the macromodel. Inaccurate passivity enforcement perturbation may cause large error adds up to the RF approximation, and may change the original design characteristics. Thus, passivity verification for larger order models requires determining the RF order, using the SVD resolved the inaccurate prior estimation of the model order, yet it yields to an exact solution. SVD is considered an expensive computational algorithm, but SVD shows accurate models order approximation. Using the SVD solved the problem associated with the passivity checking algorithm, which is estimating the initial number of poles or the model order. However, the correlation between determining the model order degree and passivity checking did not exist before. The DC frequency band and the truncation frequency point may lead to some residuals that affect the accuracy of this estimation. Using SVD to solve linear systems enhances the passivity checking of DRAM memory package and high end computers reduces the computation time.

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“…The former aim at representing attenuation and dispersion effects, while the latter are responsible for a physics-consistent representation of the finite propagation speed along the path, including the effects of multiple discontinuities causing signal reflections. DRM's can be efficiently identified using black-box fitting algorithms from tabulated frequency samples, and several methods are available for checking and enforcing their passivity [26]- [29].…”

Section: Introductionmentioning

confidence: 99%