Sparse and Passive Reduced-Order Interconnect Modeling by Eigenspace Method

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概要

• 論文の詳細を見る

• The passive and sparse reduced-order modeling of a RLC network is presented, where eigenvalues and eigenvectors of the original network are used, and thus the obtained macromodel is more accurate than that provided by the Krylov subspace methods or TBR procedures for a class of circuits. Furthermore, the proposed method is applied to low pass filtering of a reduced-order model produced by these methods without breaking the passivity condition. Therefore, the proposed eigenspace method is not only a reduced-order macromodeling method, but also is embedded in other methods enhancing their performances.

• （社）電子情報通信学会の論文
• 2008-09-01

著者

• TANJI Yuichi

  Department of Reliability-based Information Systems Engineering, Kagawa University

• Tanji Yuichi

  Department Of Reliability-based Information Systems Engineering Kagawa University

関連論文

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• Fast Simulation Technique of Plane Circuits via Two-Layer CNN-Based Modeling
• Sparse and Passive Reduced-Order Interconnect Modeling by Eigenspace Method
• Generating Stable and Sparse Reluctance/Inductance Matrix under Insufficient Discretization
• An Enhanced Time-Domain Circuit Simulation Technique Based on LIM(Numerical Analysis and Optimization)
• New Criteria of Selective Orthogonal Matrix Least-Squares Method for Macromodeling Multiport Networks Characterized by Sampled Data(Analog Circuit Techniques and Related Topics)
• Hierarchical Least-Squares Algorithm for Macromodeling High-Speed Interconnects Characterized by Sampled Data
• Sparse Realization of Passive Reduced-Order Interconnect Models via PRIMA (VLSI Design Technology and CAD)

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