Uncertainty Quantification of Waveguide Dispersion Using Sparse Grid Stochastic Testing

Gossye, Michiel; Gordebeke, Gert-Jan; Kapusuz, Kamil Yavuz; Ginste, Dries Vande; Rogier, Hendrik

doi:10.1109/tmtt.2020.2988458

Cited by 11 publications

(6 citation statements)

References 43 publications

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“…In this section, the proposed UQ framework is applied to two LNA designs, namely a 900-MHz CMOS cascode amplifier and a 2-GHz BJT amplifier. The circuits are simulated with HSPICE, which represents the full computational model (1). For the GPR prior, we consider a constant unknown trend, i.e., μ(x) = β 0 in (2), and an anisotropic Matérn 5/2 kernel (5).…”

Section: Numerical Resultsmentioning

confidence: 99%

“…. , L, are computed using the full-computational model (1). This results in the posterior Gaussian process…”

Section: The Gpr Modelmentioning

confidence: 99%

“…However, from a complex model, some closed-form estimates can be derived, with no additional effort, for the squared magnitude |y| 2 = u 2 + v 2 , which is still of interest in microwave circuits because it is associated with power/gain. 1 In particular, the sample mean of the squared magnitude is computed as…”

Section: Complex-valued Outputsmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes

Manfredi

2023

IEEE Trans. Microwave Theory Techn.

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In this article, a probabilistic machine learning framework based on Gaussian process regression (GPR) and principal component analysis (PCA) is proposed for the uncertainty quantification (UQ) of microwave circuits. As opposed to most surrogate modeling techniques, GPR models inherently carry information on the model prediction uncertainty due to unseen data. This article shows how the inherent uncertainty of GPR pointwise predictions can be combined with the uncertainty of the design parameters to provide global statistical information on the device performance with the inclusion of confidence bounds. The model confidence is possibly improved by increasing the amount of training data. In addition, PCA is employed to effectively deal with problems with multiple and possibly complex-valued output components, such as those involving the UQ of time-domain responses or multiport scattering parameters. The proposed technique is successfully applied to two low-noise amplifier designs subject to the process variation of up to 25 parameters. Comparisons against the state-of-the-art polynomial chaos expansion method demonstrates that GPR achieves superior accuracy, while additionally providing information on the prediction confidence.

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Section: Numerical Resultsmentioning

confidence: 99%

“…. , L, are computed using the full-computational model (1). This results in the posterior Gaussian process…”

Section: The Gpr Modelmentioning

confidence: 99%

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Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes

Manfredi

2023

IEEE Trans. Microwave Theory Techn.

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“…Here, we continue the work outlined in [2], where a set of sparse stochastic methods to capture LER's influence on the propagation constant of a rectangular waveguide was modeled and analyzed. In that paper, the sparse polynomial chaos (SPC) method [3] and a sparse grid version of stochastic testing (ST) [4], [5] are deployed for the waveguide analysis. Both methods strongly reduce the number of calls required to the full-wave solver, which is a finite-element method (FEM) in this study.…”

Section: Introductionmentioning

confidence: 99%

Reduced-Order Stochastic Testing of Interconnects Subject to Line Edge Roughness

Huynen

Waeytens

Bosman

et al. 2023

2023 IEEE 27th Workshop on Signal and Power Integrity (SPI)

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In this contribution we study the propagation constant of interconnects subject to line edge roughness by means of an efficient stochastic framework. By employing the stochastic testing method, we succeed in limiting the number of calls to the full-wave electromagnetic field solver at the core of the system. Additionally, the computationally burdensome solution of the eigenvalue problem is eased by reducing its order through projection on an appropriate basis. The resulting two-step acceleration leads to an effective approach to assess the effect of line edge roughness on the characteristics of interconnects. The novel framework is applied to a rough rectangular waveguide and a microstrip.

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“…In this work, the authors assume that the refractive index is a random variable, which is described by means of a Gaussian autocorrelation function. In [22], Gossye quantifies the waveguide dispersion by means of the polynomial chaos method. The effects of random roughness on wave propagation in a parallel plate metallic waveguide with finite conductivity have been studied by Ding et al [23].…”

Section: Introductionmentioning

confidence: 99%

Slab cholesteric waveguide with randomly fluctuating propagation constant

Reyes-Romero¹,

Azcárate-Yáñez²,

Rosas-Medina³

2021

J. Opt.

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In this work, we present a stochastic theoretical analysis of an electromagnetic wave propagating inside a planar slab waveguide filled with a cholesteric material. In this system, the propagation constant of the wave is assumed to be a randomly fluctuating parameter. By means of the van Kampen systematic expansion method, Maxwell’s electromagnetic equations with random coefficients, and appropriate boundary conditions, are obtained. Numerical examples of the effects of the randomness on the propagating constant, on the ratio between magnetic and electric modes, on the field profiles, and on the energy flow are given. The results show that the stochastic waveguide can support multiple Transverse Magnetic (TM) or Transverse Electric (TE) modes with the same cutoff frequency, and furthermore, they support a distribution of electric and magnetic fields with phase shift. Our results are consistent with those of an isotropic waveguide.

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Uncertainty Quantification of Waveguide Dispersion Using Sparse Grid Stochastic Testing

Cited by 11 publications

References 43 publications

Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes

Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes

Reduced-Order Stochastic Testing of Interconnects Subject to Line Edge Roughness

Slab cholesteric waveguide with randomly fluctuating propagation constant

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