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An Adaptive Partition Strategy of Galerkin Boundary Element Method for Capacitance Extraction

Published: 31 January 2023 Publication History

Abstract

In advanced process, electromagnetic coupling among interconnect wires plays an increasingly important role in signoff analysis. For VLSI chip design, the requirement of fast and accurate capacitance extraction is becoming more and more urgent. And the critical step of extracting capacitance among interconnect wires is solving electric field. However, due to the high computational complexity, solving electric field is extreme timing-consuming. The Galerkin boundary element method (GBEM) was used for capacitance extraction in [2]. In this paper, we are going to use some mathematical theorems to analysis its error. Furthermore, with the error estimation of the Galerkin method, we design a boundary partition strategy to fit the electric field attenuation. It is worth to mention that this boundary partition strategy can greatly reduce the number of boundary elements on the promise of ensuring that the error is small enough. As a consequence, the matrix order of the discretization equation will also decrease. We also provide our suggestion of the calculation of the matrix elements. Experimental analysis demonstrates that, our partition strategy obtains a good enough result with a small number of boundary elements.

References

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  1. An Adaptive Partition Strategy of Galerkin Boundary Element Method for Capacitance Extraction

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    cover image ACM Conferences
    ASPDAC '23: Proceedings of the 28th Asia and South Pacific Design Automation Conference
    January 2023
    807 pages
    ISBN:9781450397834
    DOI:10.1145/3566097
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    Published: 31 January 2023

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    Author Tags

    1. Galerkin method
    2. boundary element method
    3. boundary partition
    4. capacitance extraction

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    • National Natural Science Foundation of China (NSFC)
    • Natural Science Foundation of Fujian Province

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    ASPDAC '23 Paper Acceptance Rate 102 of 328 submissions, 31%;
    Overall Acceptance Rate 466 of 1,454 submissions, 32%

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