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Predictive methodologies for substrate parasitic extraction and modeling in heavily doped CMOS substrates

This thesis presents an automated methodology to calibrate the substrate
profile for accurate prediction of substrate parasitics using Green's function based
extractors. The technique requires fabrication of only a few test structures and results
in an accurate three layered approximation of a heavily doped epitaxial silicon
substrate. The obtained substrate resistances are accurate to about 10% of measurements.
Advantages and limitations of several common measurement techniques
used to measure substrate z-parameters and resistances are discussed. A new and
accurate z-parameter based macro-model has been developed that can be used up
to a few GHz for P��� for contacts that are as close as 2��m. This enhanced model also
addresses the limitations of previous models with regards to implementation aspects
and ease of integration in a CAD framework. Limitations of this modeling approach
have been investigated. The calibration methodology can be used along with the
scalable macromodel for a qualitative pre-design and pre-layout estimation of the
digital switching noise that couples though the substrate to sensitive analog/RF
circuits. / Graduation date: 2004

Identiferoai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/31842
Date31 July 2003
CreatorsSharma, Ajit
ContributorsFiez, Terri S., Mayaram, Kartikeya
Source SetsOregon State University
Languageen_US
Detected LanguageEnglish
TypeThesis/Dissertation

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