Fast Passivity Enforcement of $S$-Parameter Macromodels by Pole Perturbation

Deschrijver, Dirk; Dhaene, Tom

doi:10.1109/tmtt.2009.2013309

Cited by 38 publications

(43 citation statements)

References 23 publications

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“…Typically, all the elements S ij (s) of the scattering matrix representation (2) use a common denominator polynomial and pole-set [p 1 , p 2 , · · · , p K ], where such poles are either real quantities or complex conjugate pairs [8]. The identification of poles p k and residue matrices R k can be performed via the VF algorithm [8,[15][16][17][18], starting from a set of the scattering parameters under study obtained for s r = j2πf r with r = 1, . .…”

Section: Conventional State-space Modeling Of Photonic Systemsmentioning

confidence: 99%

“…Indeed, due to the unavoidable numerical approximations, the rational model computed might be non-passive. Several robust passivity enforcement methods have been proposed in the literature, see for example [16][17][18]. Now, time-domain simulations can be carried out by solving the first-order system of ordinary differential equations (ODE) (4) via suitable numerical techniques [22,23].…”

Section: Conventional State-space Modeling Of Photonic Systemsmentioning

confidence: 99%

“…It is important to note that this definition is given not only for real signals but also for complex ones. By expressing (18) in terms of the forward a(t) and backward b(t) waves, the passivity definition becomes [26,30,31] …”

Section: A Passivity Constraints On Scattering Parameters Of Basebmentioning

confidence: 99%

See 2 more Smart Citations

Numerical modeling of a linear photonic system for accurate and efficient time-domain simulations

Spina

Xing

et al. 2018

Photon. Res.

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In this paper, a novel modeling and simulation method for general linear, time-invariant, passive photonic devices and circuits is proposed. This technique, starting from the scattering parameters of the photonic system under study, builds a baseband equivalent state-space model which splits the optical carrier frequency and operates at baseband, thereby significantly reducing the modeling and simulation complexity without losing accuracy. Indeed, it is possible to analytically reconstruct the port signals of the photonic system under study starting from the time-domain simulation of the corresponding baseband equivalent model. However, such equivalent models are complex-valued systems and, in this scenario, the conventional passivity constraints are not applicable anymore. Hence, the passivity constraints for scattering parameters and state-space models of baseband equivalent systems are presented, which are essential for time-domain simulations. Three suitable examples demonstrate the feasibility, accuracy and efficiency of the proposed method.

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Section: Conventional State-space Modeling Of Photonic Systemsmentioning

confidence: 99%

Section: Conventional State-space Modeling Of Photonic Systemsmentioning

confidence: 99%

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Numerical modeling of a linear photonic system for accurate and efficient time-domain simulations

Spina

Xing

et al. 2018

Photon. Res.

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“…Finally, a time-domain model in a state-space form of the augmented system can be computed via the VF algorithm [8] and its stability and passivity can be enforced by means of standard techniques, such as [10]. Now, the equations of the terminations at the system ports are in the form where ( ), ( ) represent the vector of the port voltages and currents, respectively, is the matrix of the voltage sources and is the matrix of the port terminations.…”

Section: Stochastic Macromodeling Of General Linear Systemsmentioning

confidence: 99%

Macromodeling of general linear systems under stochastic variations

Spina

Dhaene

et al. 2016

2016 IEEE Electrical Design of Advanced Packaging and Systems (EDAPS)

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Abstract-In this paper, a stochastic modeling approach is proposed for time-domain variability analysis of general linear and passive systems with uncertain parameters. Starting from the polynomial chaos (PC) expansion of the scattering parameters, the Galerkin projections (GP) method is adopted to build an augmented scattering matrix which describes the relationship between the corresponding PC coefficients of the input and output port signals. The Vector Fitting (VF) algorithm is then used to obtain a stable and passive state-space model of such augmented matrix. As a result, a stochastic system is described by an equivalent deterministic macromodel and the time-domain variability analysis can be performed by means of one timedomain simulation. The feasibility, efficiency and accuracy of the proposed technique are verified by comparison with conventional Monte Carlo (MC) approach for a suitable numerical example.

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“…The passivity constraints for stable and causal macromodels in the scattering case require that the singular values of the transfer matrix H are unitary bounded [18,19] …”

Section: Passivity Enforcement Algorithmmentioning

confidence: 99%