<i>S</i>-Parameter De-Embedding Error Estimation Based on the Statistical Circuit Models of Fixtures
Yuanzhuo Liu, Shaohui Yong, Han Gao, Scott Hinaga, Darja Padilla, Douglas Yanagawa, James L. Drewniak, Victor Khilkevich
Abstract
S-parameter de-embedding methods require multiple fixtures to be identical. However, due to manufacturing variations, the fixtures are never perfectly identical, which violates the assumptions for the de-embedding algorithms and, in turn, introduces errors. In this article, a novel methodology is proposed to estimate the errors due to de-embedding for practical transmission line measurements. The circuit models of the thru and total lines with fixtures are created. Perturbation in the fixtures is introduced based on the fixture variation estimated by time-domain reflectometry measurements. The method can predict the envelope and estimate the confidence interval of the de-embedded insertion loss using a limited number of simulation cases.