This thesis presents an automated methodology to calibrate the substrate profile for accurate prediction of substrate parasitics using Green's function based extractors. The technique requires fabrication of only a few test structures and results in an accurate three layered approximation of a heavily doped epitaxial silicon substrate. The obtained substrate resistances are accurate to about 10% of measurements. Advantages and limitations of several common measurement techniques used to measure substrate z-parameters and resistances are discussed. A new and accurate z-parameter based macro-model has been developed that can be used up to a few GHz for P⁺ for contacts that are as close as 2μm. This enhanced model also addresses the limitations of previous models with regards to implementation aspects and ease of integration in a CAD framework. Limitations of this modeling approach have been investigated. The calibration methodology can be used along with the scalable macromodel for a qualitative pre-design and pre-layout estimation of the digital switching noise that couples though the substrate to sensitive analog/RF circuits
