Modeling Load Variations with Artificial Neural Networks to Improve On-Wafer OSLT Calibrations

We demonstrate that on-wafer open-short-load-thru (OSLT) calibrations of vector network analyzers can be improved by applying artificial neural networks (ANNs) to model the correlation between DC resistance and RF variations in load terminations. The ANNs are trained with measurement data obtained from a benchmark multiline thru-reflect-line (TRL) calibration. The open, short, and thru standards do not vary significantly from wafer to wafer, so we also model these standards using ANNs trained with calibrated measurement data chosen from an arbitrary wafer. We assess the accuracy of five OSLT calibrations with varying load terminations using the ANN-modeled standards, and find that they compare favorably (a difference of less than 0.04 in magnitude at most frequencies) to the benchmark multiline TRL calibration over a 66 GHz bandwidth. We demonstrate that ANN models offer a number of advantages over using calibrated measurement files or equivalent circuit models, including ease of use, reduced calibration times, and compactness.