The idea of macromodel was recently proposed for encrypting sensitive structures and accelerating the floating random walk (FRW)-based capacitance extraction. In the existing work, boundary element method (BEM) is employed to generate the macromodel, which might cause large error due to the violation of macromodel’s properties. To overcome this issue, we propose a modified finite difference method (FDM) with second-order electric field intensity formulas for generating the macromodel. It ensures the macromodel’s properties and thus largely improves the reliability of the macromodel-aware FRW algorithm. The numerical experiments with 3-D structures have validated our theoretic analysis, and have shown the proposed technique reliably brings more accurate capacitance results than the BEM and conventional FDM.

中文翻译：

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基于宏模型感知随机游走算法的电容提取的可靠宏模型生成

最近提出了宏模型的想法，用于加密敏感结构和加速基于浮动随机游走 (FRW) 的电容提取。在现有的工作中，使用边界元法（BEM）来生成宏模型，这可能会由于违反宏模型的性质而导致较大的误差。为了克服这个问题，我们提出了一种改进的有限差分法 (FDM)，其中包含用于生成宏模型的二阶电场强度公式。它保证了宏模型的特性，从而大大提高了宏模型感知FRW算法的可靠性。3-D 结构的数值实验验证了我们的理论分析，并表明与 BEM 和传统 FDM 相比，所提出的技术可靠地带来了更准确的电容结果。