🚀 go-pugleaf

                              Final Public Oral Examination
                          For the Degree of Doctor of Philosophy

                                     Altan Odabasioglu
                                       Dept. of ECE

                    Date:  Friday, September 10, 1999
                    Time:  2:00 pm
                    Place: Hamerschlag Hall Second Floor Conference
Room, Room 2114

                                   coffee and refreshments will be
provided

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                                                TITLE
           "PRIMA: Passive Reduced Order Interconnect Macromodeling
Algorithm"


                                            ABSTRACT
Model order reduction techniques are proposed to deal with the growing
complexity of IC
(integrated circuit) interconnect problems, ranging from simple RC trees
to complex PEEC (Partial
Element Equivalent Circuit) structures. Each type of problem requires
specific adjustments in
the model reduction methodology, however it is possible to outline the
general requirements for
use in practice. These include preservation of passivity, extension of
accuracy and error control.
The passivity property guarantees that the reduced order model will not
yield unstable results
when combined with other devices or macromodels. The accuracy extension
allows models to be
solved with only the necessary accuracy, hence optimal efficiency.
Finally, an error measure is
needed to determine the order of the approximation under certain
accuracy and macromodel complexity
constraints. In the following work a practical model order reduction
algorithm which provides
the three essential ingredients is presented. It is based on the passive
reduced-order
macromodeling algorithm (PRIMA). PRIMA is a double matrix projection
framework that provides
for robust model order reduction in terms of a proven convergence
criterion. Problem specific
cases ranging from simple RC circuits to circuits requiring multi point
expansions are
discussed and results are shown for such examples when analyzed in this
framework.