A multiparameter moment-matching model-reduction approach for generating geometrically parameterized interconnect performance models

By L. Daniel, Chin Siong Ong, Sok Chay Low, Kwok Hong Lee, Jacob K. White. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2004

357 citations recorded by the source corpus.

In this paper, we describe an approach for generating accurate geometrically parameterized integrated circuit interconnect models that are efficient enough for use in interconnect synthesis. The model-generation approach presented is automatic, and is based on a multiparameter moment matching model-reduction algorithm. A moment-matching theorem proof for the algorithm is derived, as well as a complexity analysis for the model-order growth. The effectiveness of the technique is tested using a capacitance extraction example, where the plate spacing is considered as the geometric parameter, and a multiline bus example, where both wire spacing and wire width are considered as geometric parameters. Experimental results demonstrate that the generated models accurately predict capacitance values for the capacitor example, and both delay and cross-talk effects over a reasonably wide range of spacing and width variation for the multiline bus example.

Subfields: Electrical and Electronic Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8b014ea4b8c90568b6a12666eb5a08922ecd40c1

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Citations in the corpus

Interpolation among reduced‐order matrices to obtain parameterized models for design, optimization and probabilistic analysis (2009, background): “This approach can also be paired with Krylov moment matching if all weighting coefficients that are not linear in just one parameter are replaced by additional parameters [10, 18].”
Surrogate and reduced-order modeling: a comparison of approaches for large-scale statistical inverse problems [Chapter 7] (2010, methodology): “One option is to employ linearization of the parametric dependence (Daniel et al. 2004; Grepl and Patera 2005; Veroy et al. 2003).”
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space (2008, methodology): “One can use knowledge of the application at hand to determine representative parametric inputs, as has been done to sample the parameter space for the quasi-convex optimization relaxation method [30], and to generate a POD or Krylov basis for problems in which the number of input parameters is small”
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (2015, neutral): “This differs from the earlier work in interpolatory parametric model reduction methods, such as [55, 56, 71, 119, 120], where one-sided projection is employed, i.”
Hessian‐based model reduction for large‐scale systems with initial‐condition inputs (2008, methodology): “Progress has been made in development and application of these methods to optimization applications with a small number of input parameters, for example, optimal control [11–14] and parametrized design of interconnect circuits [15].”