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
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/31842 |
| Date | 31 July 2003 |
| Creators | Sharma, Ajit |
| Contributors | Fiez, Terri S., Mayaram, Kartikeya |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
Page generated in 0.002 seconds