A neural network approach to smooth calibrated data corrupted from switching errors

Author

Landa, A.Z. ; Pulido-Gaytan, M.A. ; Reynoso-Hernandez, J.A. ; Roblin, Patrick ; Loo-Yau, J.R.

Author_Institution

Centro de Investig. Cienc. y de Educ. Super. de Ensenada (CICESE), Ensenada, Mexico

fYear

2012

fDate

29-30 Nov. 2012

Firstpage

1

Lastpage

4

Abstract

A reliable calibration of the vector network analyzer is needed in order to characterize microwave devices. For some VNAs, such as HP8510, solving the 8 error model is not enough to accurately compute the device under test (DUT) S-parameters, but also, a correction of the switching errors inherent to the measurement must be performed. In this paper, a neural network is used to find the S-parameters of the DUT free from switching errors instead of calculating the well-known equations used for that matter.

Keywords

S-parameters; calibration; computerised instrumentation; microwave devices; network analysers; neural nets; DUT S-parameters; HP8510; VNA; device under test S-parameters; microwave devices; neural network approach; smooth calibrated data; switching error correction; vector network analyzer; Artificial neural networks; Calibration; Measurement uncertainty; Neurons; Scattering parameters; Switches; Training; Vector network analyzer (VNA) calibration; artificial neural networks (ANN); switching errors;

fLanguage

English

Publisher

ieee

Conference_Titel

Microwave Measurement Symposium (ARFTG), 2012 80th ARFTG

Conference_Location

San Diego, CA

Print_ISBN

978-1-4673-4817-1

Electronic_ISBN

978-1-4673-4820-1

Type

conf

DOI

10.1109/ARFTG.2012.6422428

Filename

6422428