Authors: Arjun Mullaguru, Huang Huang, Xiaolong Yuan, Jeffrey Fan
Addresses: Department of Electrical and Computer Engineering, Florida International University, Miami, Florida, USA. ' Department of Electrical and Computer Engineering, Florida International University, Miami, Florida, USA. ' School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China. ' Department of Electrical and Computer Engineering, Florida International University, Miami, Florida, USA
Abstract: 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.
Keywords: model order reduction; passive reduced order interconnect macromodelling algorithm; PRIMA; eigen decomposition; rational transfer function fitting; eigenmode analysis; residues.
International Journal of Computer Applications in Technology, 2011 Vol.41 No.1/2, pp.28 - 33
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 01 Sep 2011 *