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A new adaptive multi‐bias S ‐parameter measurement algorithm for transistor characterization

A new adaptive multi‐bias S ‐parameter measurement algorithm for transistor characterization A new adaptive measurement algorithm is described for the control of an automated S‐parameter measurement set‐up used to characterize transistors for non‐linear modelling. The procedure differs from previous algorithms in that is uses both the device DC‐ and S‐parameter data to identify DC bias regions where the device characteristics are changing rapidly. By placing more bias points in these areas and less data points in regions where the device response stays constant, the non‐linear behaviour of the device can be characterized more accurately while keeping the total volume of the experimental data and hence the measurement time to an acceptable level. Experimental results are presented that illustrates the operation of the adaptive algorithm as well as the influence that the selection procedure has on non‐linear modelling results. Copyright © 2005 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Numerical Modelling: Electronic Networks, Devices and Fields Wiley

A new adaptive multi‐bias S ‐parameter measurement algorithm for transistor characterization

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References (20)

Publisher
Wiley
Copyright
Copyright © 2005 John Wiley & Sons, Ltd.
ISSN
0894-3370
eISSN
1099-1204
DOI
10.1002/jnm.577
Publisher site
See Article on Publisher Site

Abstract

A new adaptive measurement algorithm is described for the control of an automated S‐parameter measurement set‐up used to characterize transistors for non‐linear modelling. The procedure differs from previous algorithms in that is uses both the device DC‐ and S‐parameter data to identify DC bias regions where the device characteristics are changing rapidly. By placing more bias points in these areas and less data points in regions where the device response stays constant, the non‐linear behaviour of the device can be characterized more accurately while keeping the total volume of the experimental data and hence the measurement time to an acceptable level. Experimental results are presented that illustrates the operation of the adaptive algorithm as well as the influence that the selection procedure has on non‐linear modelling results. Copyright © 2005 John Wiley & Sons, Ltd.

Journal

International Journal of Numerical Modelling: Electronic Networks, Devices and FieldsWiley

Published: Jul 1, 2005

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