Sensor Based Modeling of Copper Chemical Mechanical Planarization

The variations in slurry chemistry parameters
including pH, concentrations of complexing and
corrosion inhibiting agents, and slurry flow rates
as well as average MRR in Cu-CMP process are modeled
using features from two vibration sensors (one wired
and the other wireless accelerometer). The study is
perhaps one of the first efforts to use wireless
vibration sensors for real-time monitoring of MRR
and the chief slurry chemistry parameters in CMP. We
hope that this work will lead to the advent of
physics-based process-machine interaction (PMI)
models that can be used to delineate the various
process behaviors, which in turn can be used to
select vibration signal features that are more
sensitive to variations in MRR and other quality and
performance variable of the CMP process. The study
has delineated the joint (i.e., statistical
interaction) effects of various slurry components on
MRR in Cu-CMP. In specific, the joint effects of the
complexing agent with other input parameters, such
as pH and flow rate (i.e., two-way interactions) on
MRR have been found to be significant.

Autorentext

M.S. Oklahoma State University, May 2008B.E. University of Pune,India, August 2005Awards: 1.First prize for research poster at 18th annual research symposium held at Oklahoma State University, 2006 2.Awarded a travel grant to present at the 2008 NSF-CMMI Engineering Research and Innovation Conference, January 2008, Knoxville,TN.

Klappentext

The variations in slurry chemistry parameters including pH, concentrations of complexing and corrosion inhibiting agents, and slurry flow rates as well as average MRR in Cu-CMP process are modeled using features from two vibration sensors (one wired and the other wireless accelerometer). The study is perhaps one of the first efforts to use wireless vibration sensors for real-time monitoring of MRR and the chief slurry chemistry parameters in CMP. We hope that this work will lead to the advent of physics-based process-machine interaction (PMI) models that can be used to delineate the various process behaviors, which in turn can be used to select vibration signal features that are more sensitive to variations in MRR and other quality and performance variable of the CMP process. The study has delineated the joint (i.e., statistical interaction) effects of various slurry components on MRR in Cu-CMP. In specific, the joint effects of the complexing agent with other input parameters, such as pH and flow rate (i.e., two-way interactions) on MRR have been found to be significant.