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A Recovery Algorithm of Linear Complexity in the Time-Domain Layered Finite Element Reduction Recovery (LAFE-RR) Method for Large Scale Electromagnetic Analysis of High-Speed ICs

机译:一种时域分层有限元减少恢复(Lafe-RR)方法的线性复杂度恢复算法,用于高速IC的大规模电磁分析

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Time-domain layered finite element reduction recovery (LAFE-RR) method was recently developed for large scale electromagnetic analysis of high-speed ICs. This method is capable of analytically and rigorously reducing the system matrix of a 3D multilayer circuit to that of a single layer one irrespective of the original problem size. In addition, it preserves the sparsity of the original system matrix. In this paper, we propose an efficient algorithm to recover the volume unknowns in the time-domain LAFE-RR method. This algorithm constitutes a direct solution of the matrix formed by volume unknowns in each layer. This direct solution possesses a linear complexity in both CPU run time and memory consumption. The cost of matrix factorization is negligible. The cost of matrix solution is linear. Numerical and experimental results have demonstrated the accuracy and efficiency of the proposed algorithm.
机译:最近开发了时域分层有限元减少恢复(LAFE-RR)方法,用于高速IC的大规模电磁分析。不论原始问题大小如何,该方法能够分析和严格地将3D多层电路的系统矩阵减小到单层的系统矩阵。此外,它保留了原始系统矩阵的稀疏性。在本文中,我们提出了一种有效的算法,以在时域Lafe-RR方法中恢复体积未知数。该算法构成由每个层中的体积未知数形成的矩阵的直接解决方案。这种直接解决方案在CPU运行时间和内存消耗中具有线性复杂性。矩阵分解的成本可忽略不计。矩阵溶液的成本是线性的。数值和实验结果表明了所提出的算法的准确性和效率。

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