Uniformly Stable Parameterized Macromodeling Through Positive Definite Basis Functions

Zanco, Alessandro; Grivet-Talocia, S.; Bradde, Tommaso; Stefano, M. De

doi:10.1109/tcpmt.2020.3012275

Cited by 10 publications

(12 citation statements)

References 51 publications

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“…From the numerical standpoint, (31) cannot be enforced directly, as it embeds an infinite number of constraints that must be verified over the entire continuous frequency-parameter space. Its enforcement has been addressed either by discretization into a finite set via sampling-based strategies [23], or by deriving over-conservative sufficient conditions on the sign of individual terms in the denominator expansion [24]. These two strategies are complementary from the point of view of the modeling performances: while the former guarantees high level of accuracy but does not provide a theoretical guarantee for uniform stability, the latter leads to provable stability and is very efficient but can result in accuracy degradation due to the approximate nature of the applied constraints.…”

Section: Uniform Stability Conditionsmentioning

confidence: 99%

“…The circuit depends on d = 7 design parameters, which are listed in Table 1. The device was first presented in [42]; additionally, it was considered as a test-bench for the generation of uniformly stable parameterized macromodels in [24].…”

Section: An Active Devicementioning

confidence: 99%

“…First, we show that even in case of high-dimensional design spaces, the generation of uniformly stable parameterized macromodels can be efficiently tackled by solving problem (40). Second, we show how the proposed approach is less conservative than the current state-of-the-art method [24] providing a formal guarantee of uniform stability given model structure (13). Of uniform is applicable since this is an active device.…”

Section: An Active Devicementioning

confidence: 99%

“…In order to show the low degree of conservativity of the proposed stability constraints, we repeated the same experiment performed in [24], where the uniform stability is enforced by imposing a sign inequality directly on the denominator coefficients r n,ℓ during the model generation. For this purpose, we considered the same LNA device and restricted the dimension of the design space to d = 5, by taking into account only the first five parameters listed in Table 1.…”

Section: An Active Devicementioning

confidence: 99%

“…Uni-form stability can be enforced by embedding some constraints in the fitting process. These constraints can be either based on (adaptive) sampling in the parameter space [22], [23], or by imposing some sign properties in the model coefficients [24]. The former approach may miss small stability violations due to the finite number of constraints that can be constructed, while the latter is known to be over-conservative and may lead to a model with reduced accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Extraction of Uniformly Stable and Passive Parameterized Macromodels

et al. 2022

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A Robust algorithm for the extraction of reduced-order behavioral models from sampled frequency responses is proposed. The system under investigation can be any Linear and Time Invariant structure, although the main emphasis is on devices that are relevant for Signal and Power Integrity and RF design, such as electrical interconnects and integrated passive components. We assume that the device under modeling is parameterized by one or more design variables, which can be related to geometry or materials. Therefore, we seek for multivariate macromodels that reproduce the dynamic behavior over a predefined frequency band, with an explicit embedded dependence of the model equations on these external parameters. Such parameterized macromodels may be used to construct component libraries and prove very useful in fast system-level numerical simulations in time or frequency domain, including optimization, what-if, and sensitivity analysis. The main novel contribution is the formulation of a finite set of convex constraints that are applied during model identification, which provide sufficient conditions for uniform model stability and passivity throughout the parameter space. Such constraints are characterized by an explicit control allowing for a trade-off between model accuracy and runtime, thanks to some special properties of Bernstein polynomials. In summary, we solve the longstanding problem of multivariate stability and passivity enforcement in data-driven model order reduction, which insofar has been tackled only via either overconservative or heuristic and possibly unreliable methods.

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Section: Uniform Stability Conditionsmentioning

confidence: 99%

Section: An Active Devicementioning

confidence: 99%

Section: An Active Devicementioning

confidence: 99%

Section: An Active Devicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Extraction of Uniformly Stable and Passive Parameterized Macromodels

et al. 2022

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On checking dissipativity of parameterized linear and time‐invariant circuits and systems

Zanco

Grivet-Talocia

2022

Circuit Theory & Apps

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A novel dissipativity characterization is proposed for linear time-invariant circuits and systems whose dynamic behavior depends on one external parameter. The proposed formulation extends to the parameterized case standard Hamiltonian-based algebraic dissipativity characterizations and is structured as an underdetermined multiparameter eigenvalue problem. Resulting from a polynomial parameterization, the proposed characterization leads to an effective set of algorithms that are able to determine the regions of local dissipativity and local activity in the frequency-parameter plane, which in turn can be exploited by dissipativity enforcement algorithms to produce uniformly passive models. The reference application of proposed technique is behavioral modeling of complex circuits or systems, whose dynamic behavior can be compressed into a low-order system through dedicated model order-reduction processes. Various examples ranging from integrated components to microstrip filters and networks are used to illustrate the proposed characterization.

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Parameterized Surrogate Models of Microstrip Structures for Electromagnetic-Based Power Amplifier Design and Statistical Analysis

Ramella

Zanco

Stefano

et al. 2023

Lecture Notes in Electrical Engineering

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Uniformly Stable Parameterized Macromodeling Through Positive Definite Basis Functions

Cited by 10 publications

References 51 publications

Data-Driven Extraction of Uniformly Stable and Passive Parameterized Macromodels

Data-Driven Extraction of Uniformly Stable and Passive Parameterized Macromodels

On checking dissipativity of parameterized linear and time‐invariant circuits and systems

Parameterized Surrogate Models of Microstrip Structures for Electromagnetic-Based Power Amplifier Design and Statistical Analysis

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