三维电容提取的快速分层算法

Weiping Shi, Jianguo Liu, N. Kakani, T. Yu
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引用次数: 106

摘要

提出了一种计算复杂结构的三维完美电导体电容的新算法。与以前的最佳算法相比,新算法速度快得多,使用的内存少得多,并且与内核无关。该算法基于求解n体问题的分层算法,是求解电容提取问题积分方程的边界元法的一种加速。该算法首先根据电位系数的估计和用户提供的误差界自适应地将导体表面细分为面板。该算法将潜在系数矩阵存储在一个大小为O(n)的分层数据结构中,尽管如果显式展开,矩阵的大小为n/sup 2/,其中n是面板的数量。分层数据结构允许我们在O(n)时间内将系数矩阵与任何向量相乘。最后,我们使用广义最小残差算法在O(mn)时间内求解了m个大小为n/spl × /n的线性系统,其中m为导体数。实现了新算法,并与已有的最佳算法进行了性能比较。对于k/spl times/k总线示例,我们的算法比FastCap快100到40倍,并且使用FastCap使用的1/100到1/60的内存。新算法计算的结果与FastCap计算的结果误差在2.7%以内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A fast hierarchical algorithm for 3-D capacitance extraction
We present a new algorithm for computing the capacitance of three-dimensional perfect electrical conductors of complex structures. The new algorithm is significantly faster and uses much less memory than previous best algorithms, and is kernel independent. The new algorithm is based on a hierarchical algorithm for the n-body problem, and is an acceleration of the boundary element method for solving the integral equation associated with the capacitance extraction problem. The algorithm first adaptively subdivides the conductor surfaces into panels according to an estimation of the potential coefficients and a user-supplied error bound. The algorithm stores the potential coefficient matrix in a hierarchical data structure of size O(n), although the matrix is size n/sup 2/ if expanded explicitly, where n is the number of panels. The hierarchical data structure allows us to multiply the coefficient matrix with any vector in O(n) time. Finally, we use a generalized minimal residual algorithm to solve m linear systems each of size n/spl times/n in O(mn) time, where m is the number of conductors. The new algorithm is implemented and the performance is compared with previous best algorithms. For the k/spl times/k bus example, our algorithm is 100 to 40 times faster than FastCap, and uses 1/100 to 1/60 of the memory used by FastCap. The results computed by the new algorithm are within 2.7% from that computed by FastCap.
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