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Second-Order_Arnoldi_Reduction_using_Weighted_Gaussian_Kernel.pdf
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ArticleKAUST Department
Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi ArabiaComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Date
2022-04-18Permanent link to this record
http://hdl.handle.net/10754/676310
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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.Citation
Malik, R., Alam, M., Muhammad, S., Duraihem, F. Z., & Massoud, Y. (2022). Second-Order Arnoldi Reduction using Weighted Gaussian Kernel. IEEE Access, 1–1. https://doi.org/10.1109/access.2022.3167732Sponsors
Supported by Mirpur University of Science and Technology (MUST), Mirpur - 10250, AJK, Pakistan, University of Poonch Rawalakot, AJK, 12350, Pakistan, Deanship of Scientific Research at King Saud University for funding this work through research group no.RG-1441-351 and Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi ArabiaPublisher
IEEEJournal
IEEE AccessAdditional Links
https://ieeexplore.ieee.org/document/9758797/https://ieeexplore.ieee.org/document/9758797/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9758797
ae974a485f413a2113503eed53cd6c53
10.1109/ACCESS.2022.3167732
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