Second-Order Arnoldi Reduction using Weighted Gaussian Kernel

Rahila Malik, Mehboob Alam, Shah Muhammad, Faisal Zaid Duraihem, Yehia Mahmoud Massoud

Research output: Contribution to journalArticlepeer-review

Abstract

Modeling and design of on-chip interconnect continue to be a fundamental roadblock for high-speed electronics. The continuous scaling of devices and on-chip interconnects generates self and mutual inductances, resulting in generating second-order dynamical systems. The model order reduction is an essential part of any modern computer-aided design tool for prefabrication verification in the design of on-chip components and interconnects. The existing second-order reduction methods use expensive matrix inversion to generate orthogonal projection matrices and often do not preserve the stability and passivity of the original system. In this work, a second-order Arnoldi reduction method is proposed, which selectively picks the interpolation points weighted with a Gaussian kernel in the given range of frequencies of interest to generate the projection matrix. The proposed method ensures stability and passivity of the reduced-order model over the desired frequency range. The simulation results show that the combination of multi-shift points weighted with Gaussian kernel and frequency selective projection dynamically generates optimal results with better accuracy and numerical stability compared to existing reduction techniques.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Access
DOIs
StatePublished - Apr 18 2022

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)
  • Materials Science(all)

Fingerprint

Dive into the research topics of 'Second-Order Arnoldi Reduction using Weighted Gaussian Kernel'. Together they form a unique fingerprint.

Cite this