Utilizing macromodels in floating random walk based capacitance extraction

This paper presents techniques that use macromodels in order to extend and improve the floating random walk (FRW) method for capacitance extraction. A macromodel is built for each sub-structure for which it is necessary or convenient to hide its geometry details during capacitance extraction. Then, a macromodel-aware random walk scheme connects the Markovchain random walk inside the macromodels and the FRW outside through scalable blank patch regions. This method can be used for instance to extract capacitances for structure with encrypted sub-structures, and extend the FRW method's capability for structure with complex geometry or repeated layout patterns. Numerical results validate the merits of the proposed method with structures including encrypted FinFET layout, complex geometry features, and cyclic layout patterns.