Stable and efficient reduction of substrate model networks using congruence transforms
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
PRIMA: passive reduced-order interconnect macromodeling algorithm
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Characterizing Substrate Coupling in Deep-Submicron Designs
IEEE Design & Test
Krylov subspace techniques for reduced-order modeling of large-scale dynamical systems
Applied Numerical Mathematics
On symbolic model order reduction
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Hi-index | 0.00 |
In this paper, we have proposed a novel model order reduction technique via rational transfer function fitting and eigenmode analysis considering residues. We define a constant as a key in the sorting algorithm as one of correlations in order to sort the order of eigenvalues. It is demonstrated that the accuracy via eigenmode analysis considering residues is improved. The proposed algorithm is a general method to match pole values with frequency domain poles for linear RC and RLC systems. Calculation of pole eigenvalues and eigen vectors can be done with more sophisticated analysis with the same level or smaller cost in the proposed algorithms in comparison to passive reduced order interconnect macromodeling algorithm (PRIMA). The experimental results show that our algorithm reduces up to 90% errors compared to the existing model order reduction algorithm, such as PRIMA, in wide frequency environment with the same number of poles in comparison.