Artificial Neural Network Modeling for Improved On-Wafer Line-Reflect-Match Calibrations
Jeffrey Jargon, Kuldip Gupta
We model a load using an artificial neural network (ANN) to improve an on-wafer line-reflect-match (LRM) calibration of a vector network analyzer. The ANN is trained with measurement data obtained from a thru-reflect-line (TRL) calibration. The accuracy of the LRM calibration using the ANN-modeled load compares favorably to a benchmark multiline TRL calibration with an average worst-case scattering parameter error bound of 0.017 over a 40 GHz bandwidth.