Propagation of Linear Uncertainties through Multiline Thru-Reflect-Line Calibration

TL;DR

This paper introduces a linear uncertainty propagation method for multiline TRL calibration, validated against Monte Carlo simulations, offering a more efficient way to assess calibration uncertainties in vector network analyzers.

Contribution

It presents a novel linear approach for propagating uncertainties in multiline TRL calibration, aligning with ISO GUM guidelines, and validates it through Monte Carlo analysis.

Findings

Linear uncertainty propagation agrees with Monte Carlo results

Method improves efficiency of uncertainty assessment

Applicable to various measurement uncertainties

Abstract

This study proposes a linear approach for propagating uncertainties in the multiline thru-reflect-line (TRL) calibration method for vector network analyzers. The multiline TRL formulation we are proposing applies the law of uncertainty propagation as outlined in the ISO Guide to the Expression of Uncertainty in Measurement (GUM) to both measurement and model uncertainties. In addition, we conducted a Monte Carlo analysis using a combination of measured and synthetic data to model various uncertainties, such as additive noise, reflect asymmetry, line mismatch, and line length offset. The results of our linear uncertainty formulation demonstrate agreement with the Monte Carlo method and provide a more efficient means of assessing the uncertainty budget of the multiline TRL calibration.