In this contribution we present an approach to reduce the error arising from the variations of the lumped load, due to process spread, in probe level calibrations. First, full-wave electromagnetic (EM) simulations are employed to generate the nominal standard responses, then a parametric EM simulation of the load structure is used to generate a parametrized model of the standard. The approach is tested using a Short-Open-Load-Reciprocal calibration algorithm and an impedance standard calibration substrates developed on a 150 mm Quartz wafer (400 pm thick). In this process the high fidelity of the lateral dimension of the fabricated structures, realized using IC Photolithography, allows to confine the variations of the load response to only the thin-film resistor thickness spread. The DC response of the load, measured during the calibration step, is used to identify the specific RF response of the probed load from the parametric model. A complete analysis using full-wave EM simulations accounting for process variation is presented together with a set of experimental data up to 67GHz.@en

In this contribution, we describe the modeling approaches and the characterization procedures used to develop accurate standard models for cryogenic, probe-level, calibrations substrates.The key electrical and mechanical parameters of the impedance terminations and the lines used in commercially available impedance standard substrates are first characterized versus temperature. After, these component are simulated using 2.5D EM solvers including their mechanical variation when exposed to cryogenic temperatures, to extract their nominal response at 7 Kelvin. The quality of the resulting calibrations at cryogenic is evaluated first using independent CPW lines on the calibration substrates and then by measuring the response of a transformer-based resonator realized on a Si-based technology.Ambient temperature models are used as a comparison, to highlight the accuracy improvement that can be achieved employing optimized Cryo-EM based models. @en