Device model accuracy not only depends on the model itself but also on the procedure used for parameter extraction and on the experimental data collected for the extraction. In analog circuits, many of the performances depend on the small signal parameters of the devices rather than on the static current (for example the gain of a differential amplifier). Hence experimental measurements of small signal parameters must be collected to increase model accuracy. In this paper an extraction procedure that uses experimental data both on drain current and on small signal parameters of MOSFET’s is proposed. It is shown the correspondence between the inclusion of small signal parameters in the optimization procedure and the use of the off-diagonal terms of the covariance matrix in the Maximum Likelihood Estimation Method.

A methodology for statistical parameter extraction from DC and small signal measurements

CONTI, MASSIMO;CRIPPA, Paolo;ORCIONI, Simone;TURCHETTI, Claudio
1999

Abstract

Device model accuracy not only depends on the model itself but also on the procedure used for parameter extraction and on the experimental data collected for the extraction. In analog circuits, many of the performances depend on the small signal parameters of the devices rather than on the static current (for example the gain of a differential amplifier). Hence experimental measurements of small signal parameters must be collected to increase model accuracy. In this paper an extraction procedure that uses experimental data both on drain current and on small signal parameters of MOSFET’s is proposed. It is shown the correspondence between the inclusion of small signal parameters in the optimization procedure and the use of the off-diagonal terms of the covariance matrix in the Maximum Likelihood Estimation Method.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/42841
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