A Stable Bayesian Vector Network Analyzer Calibration Algorithm

• Published in: IEEE transactions on microwave theory and techniques Vol. 57; no. 4; pp. 869 - 880
• Main Authors: Hoffmann, J., Leuchtmann, P., Ruefenacht, J., Vahldieck, R.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Applied sciences; Bayesian methods; Calibration; Carbon nanotubes; Computer errors; Devices; Electronics; error analysis; Errors; estimation; Exact sciences and technology; Impedance; Instruments; Mathematical models; Measurement standards; Molecular electronics, nanoelectronics; Monte Carlo methods; Networks; Robustness; Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices; Testing; vector network analyzer (VNA); Network analyzer; vector network analyzer (VNA); Error analysis; Error estimation; Carbon nanotubes; Implementation; Optimization; Nanoelectronics; Accuracy; Monte Carlo methods; Bayes network; Robustness; Monte Carlo method; Nanotube devices; Benchmarking; Transistor; Nonlinear problems; Calibration; Vector method; Algorithm; estimation; Statistical model; Numerical simulation; Bayes methods; High impedance
• ISSN: 0018-9480; 1557-9670
• DOI: 10.1109/TMTT.2009.2015096

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.