Reduced-order modeling of large linear subcircuits via a block Lanczos algorithm
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Matrix computations (3rd ed.)
Efficient model reduction of interconnect via approximate system gramians
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
A factorization-based framework for passivity-preserving model reduction of RLC systems
Proceedings of the 39th annual Design Automation Conference
Computer Methods for Circuit Analysis and Design
Computer Methods for Circuit Analysis and Design
An Implicitly Restarted Symplectic Lanczos Method for the Symplectic Eigenvalue Problem
SIAM Journal on Matrix Analysis and Applications
Efficient Approximate Balanced Truncation of General Large-Scale RLC Systems via Krylov Methods
ASP-DAC '02 Proceedings of the 2002 Asia and South Pacific Design Automation Conference
Algorithms for model reduction of large scale rlc systems
Algorithms for model reduction of large scale rlc systems
PRIMA: passive reduced-order interconnect macromodeling algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Guaranteed passive balancing transformations for model order reduction
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Modeling Interconnect Variability Using Efficient Parametric Model Order Reduction
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Sparse and Passive Reduced-Order Interconnect Modeling by Eigenspace Method
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
| Hi-index | 0.00 |
This paper presents an efficient t o-stage project-and-balance scheme for passivity-preserving model order reduction. Orthogonal dominant eigenspace projection is implemented by integrating the Smith method and Krylov subspace iteration. It is followed by stochastic balanced truncation herein a novel method, based on the complete separation of stable and unstable invariant subspaces of a Hamiltonian matrix, is used for solving two dual algebraic Riccati equations at the cost of essentially one. A fast-converging quadruple-shift bulge-chasing SR algorithm is also introduced for this purpose. Numerical examples confirm the quality of the reduced-order models over those from conventional schemes.