Analytical integration-based causality checking of tabulated S-parameters

Asgari, Saeed; Lalgudi, S.N.; Tsuk, Michael

doi:10.1109/epeps.2010.5642578

Cited by 2 publications

(8 citation statements)

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“…In [6], the contribution of interpolation error in D n (ω) is accounted for as follows. It uses piecewise polynomials to represent H ( j ω)…”

Section: B Analytical Integration-based Procedures With Piecewisepolymentioning

confidence: 99%

“…The details behind the derivation of (15) are provided in [6] and [7]. The discretization error bound is computed as…”

Section: B Analytical Integration-based Procedures With Piecewisepolymentioning

confidence: 99%

“…These new procedures, however, suffer from some issues. For example, the scheme in [6] can lead to unbounded values if there are highly nonuniform frequency steps in the tabulated data. In [7], this problem is resolved; however, it can be slow for data with large propagation delays.…”

Section: Introductionmentioning

confidence: 98%

“…This paper first summarizes the prior work and extends the causality checking procedure in [6] to use a numerical integration approach like in [3]. There are three reasons for this extension: 1) like [7], the extension does not suffer from the unboundedness problem observed in [6]; 2) the new procedure is much faster than [7], especially when there are large propagation delays in data; and 3) for example, discretization error bound from this extension is smaller than that from [7]. For completeness, this paper also extends [7] to use a numerical integration approach.…”

Section: Introductionmentioning

confidence: 99%

“…Two new testing procedures were proposed in [6] and [7] to minimize false-positive test outcomes. They do so by properly accounting for in the discretization error bound the error incurred while approximating tabulated data by interpolation/approximation functions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

On Checking Causality of Tabulated $S$-Parameters

Lalgudi

2013

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

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The existing formulation for the causality testing of tabulated S-parameters can be oversensitive to noncausality, in that it can report causality violations for data that may not result in an inaccurate transient simulation. Existing numerical procedures that implement the present formulation can result in false-positive test outcomes (i.e., causal data being declared as noncausal). A mechanism to reduce the oversensitivity in the formulation and new testing procedures that minimize false positives are proposed. Oversensitivity is reduced significantly if the target fitting error (of the macromodel to tabulated data) is chosen as the minimum amplitude of the noncausality to be detected. Sources of false-positive outcomes are identified, and four procedures that minimize false positives are proposed. These procedures differ in resolution, accuracy, and computational requirements. Numerical results demonstrating the proposed modifications to the formulation and numerical results comparing and contrasting the different procedures are presented.

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“…In [6], the contribution of interpolation error in D n (ω) is accounted for as follows. It uses piecewise polynomials to represent H ( j ω)…”

Section: B Analytical Integration-based Procedures With Piecewisepolymentioning

confidence: 99%

“…The details behind the derivation of (15) are provided in [6] and [7]. The discretization error bound is computed as…”

Section: B Analytical Integration-based Procedures With Piecewisepolymentioning

confidence: 99%