A Stable Bayesian Vector Network Analyzer Calibration Algorithm

Abstract

A new overdetermined vector network analyzer (VNA) calibration algorithm is presented. The new algorithm shows significant advantages in the measurement of very high-impedance devices such as carbon nanotube transistors and can be applied to all types of VNA calibration. It was found that, for high-impedance devices, the new algorithm yields up to four times more accurate results. The focus of this study is on the accuracy and robustness of the algorithm. A statistical error model of calibration, which includes errors in the calibration standards and errors in the VNA, is converted into a formula for calibration by Bayes' theorem. The numerical implementation of this formula makes use of nonlinear optimization techniques and Monte Carlo integration. The resulting new algorithm is compared against various other algorithms. Benchmarking shows that the presented calibration algorithm is robust and more accurate than all other tested algorithms in all tested calibration scenarios.