Variability Analysis via Parameterized Model Order Reduction and Numerical Inversion of Laplace Transform

Tao, Ye; Nouri, Behzad; Nakhla, M.; Farhan, Mina; Achar, R.

doi:10.1109/tcpmt.2016.2642199

Cited by 23 publications

(14 citation statements)

References 20 publications

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“…Parametrized model-order reduction (PMOR) is a concept that was born out of MOR and was used in the framework of linear circuit analysis to accelerate tasks such as uncertainty quantification and variability analysis [106]. PMOR was recently extended to nonlinear circuits in the context of DC analysis [102].…”

Section: Future Trendsmentioning

confidence: 99%

Simulation and Automated Modeling of Microwave Circuits: State-of-the-Art and Emerging Trends

et al. 2021

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Section: Future Trendsmentioning

confidence: 99%

Simulation and Automated Modeling of Microwave Circuits: State-of-the-Art and Emerging Trends

et al. 2021

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“…The coefficients of the polynomials N N (z) and D M (z) are available in closed form [15], and the poles and residues are readily computed from these. By substituting (8) into (7), the time-domain response can be expressed as [14]…”

Section: B Nilt Based On Padé Approximationmentioning

confidence: 99%

“…where from (14), the time-domain TM is computed as T Fig. 2 shows the spread of the response resulting from the parameter uncertainty and obtained based on 10000 Monte Carlo (MC) samples.…”

Section: A Illustrative Example: Control Loopmentioning

confidence: 99%

Self-Validated Time-Domain Analysis of Linear Systems With Bounded Uncertain Parameters

Trinchero

Manfredi

Stievano

2018

IEEE Trans. Circuits Syst. II

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This paper presents a novel approach to predict the bounds of the time-domain response of a linear system subject to multiple bounded uncertain input parameters. The method leverages the framework of Taylor models in conjunction with the numerical inversion of Laplace transform (NILT). Different formulations of the NILT are reviewed, and their advantages and limitations are discussed. An implementation relying on an inverse fast Fourier transform (IFFT) turns out to be the most efficient and accurate alternative. The feasibility of the technique is validated based on several diverse application examples, namely a control loop, a lossy transmission-line network and an active low-pass filter.

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“…Model-order reduction (MOR) has proven to be an effective tool in reducing the computational complexity of simulating large systems. MOR has been successfully used in a broad spectrum of linear and nonlinear applications [41][42][43][44][45][46][47][48] such as microelectronics [39,40,[49][50][51], high-speed and RF circuits [52][53][54][55], uncertainty quantification [9,[56][57][58], electromagnetic [59,60] and thermal analysis [61]. The recent evolution of MOR techniques have also been fueled by their popularity and success in broader fields such as mechanical, biomedical, civil, and aerospace engineering [62][63][64].…”

Section: Techniquesmentioning

confidence: 99%

“…Moment matching PMOR has been utilized successfully in linear circuits for purposes analogous to the DC operating point (e.g., computing frequency response) [8][9][10][11][12]. Part of the popularity of moment matching in linear circuits is the low computational cost of constructing the moments projection basis [13,14].…”

Section: Introduction 11 Background and Motivationmentioning

confidence: 99%

Interpolatory Nonlinear Model Order Reduction and its Application in Circuit Simulation

Nouri¹

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This thesis presents a new approach to construct parametrized reduced-order models for nonlinear circuits. The reduced model is obtained such that it matches the variations in the DC operating point of the original full circuit in response to variations in several of its key design parameters. The new approach leverages, through the use of circuit moments with respect to the design parameters, the discrete empirical interpolation approach developed for model reduction in other domains and enables its efficient application to the problem of DC operating point in nonlinear circuits.

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Variability Analysis via Parameterized Model Order Reduction and Numerical Inversion of Laplace Transform

Cited by 23 publications

References 20 publications

Simulation and Automated Modeling of Microwave Circuits: State-of-the-Art and Emerging Trends

Simulation and Automated Modeling of Microwave Circuits: State-of-the-Art and Emerging Trends

Self-Validated Time-Domain Analysis of Linear Systems With Bounded Uncertain Parameters

Interpolatory Nonlinear Model Order Reduction and its Application in Circuit Simulation

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