Modeling Interconnect Variability Using Efficient Parametric Model Order Reduction
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
SPRIM: structure-preserving reduced-order interconnect macromodeling
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Variational interconnect analysis via PMTBR
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Random sampling of moment graph: a stochastic Krylov-reduction algorithm
Proceedings of the conference on Design, automation and test in Europe
PRIMA: passive reduced-order interconnect macromodeling algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Poor man's TBR: a simple model reduction scheme
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
SPARE: a scalable algorithm for passive, structure preserving, parameter-aware model order reduction
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems - Special issue on the 2009 ACM/IEEE international symposium on networks-on-chip
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In this paper we describe a flexible and efficient new algorithm for model order reduction of parameterized systems. The method is based on the reformulation of the parametric system as a parallel interconnection of the nominal transfer function and the non-parametric transfer function sensitivities with respect to the parameter variations. Such a formulation reveals an explicit dependence on each parameter which is exploited by reducing each component system independently via a standard non-parametric structure preserving algorithm. Therefore, the resulting smaller size interconnected system retains the structure of the original with respect to parameter dependence. This allows for better accuracy control, enabling independent adaptive order determination with respect to each parameter and adding flexibility in simulation environments. It is shown that the method is efficiently scalable and preserves relevant system properties such as passivity. The new technique can handle fairly large parameter variations on systems whose outputs exhibit smooth dependence on the parameters. Several examples show that besides the added flexibility and control, when compared with competing algorithms, the proposed technique can, in some cases, produce smaller reduced models with potential accuracy gains.