Reliable Macromodel Generation for the Capacitance Extraction Based on Macromodel-Aware Random Walk Algorithm

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.