Sensor-based Modeling and Monitoring of Chemical Mechanical Polishing
Analysis of Experimental Sensor Data Integrating Statistical Time Series Analysis and Nonlinear Dynamics (Chaos Theory) Paradigms
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This book provides a framework for real time controlof the Chemical Mechanical Planarization (CMP)process based on combining nonlinear dynamicsprinciples with statistical process monitoringapproaches. CMP has a directbearing on the computational speed and dimensionalcharacteristics of solid state devices. The challengein CMP may be narrowed to domains envelopingproductivity, measured in terms of material removalrate (MRR), and quality which is usually specified interms of surface roughness - Ra, within wafernon-uniformity (WIWNU), defect rate, etc. In thiswork, experimental investigations of C...
This book provides a framework for real time control
of the Chemical Mechanical Planarization (CMP)
process based on combining nonlinear dynamics
principles with statistical process monitoring
approaches. CMP has a direct
bearing on the computational speed and dimensional
characteristics of solid state devices. The challenge
in CMP may be narrowed to domains enveloping
productivity, measured in terms of material removal
rate (MRR), and quality which is usually specified in
terms of surface roughness - Ra, within wafer
non-uniformity (WIWNU), defect rate, etc. In this
work, experimental investigations of CMP are executed
with the aid of sensors. The analysis of the data
reveals the presence of pronounced stochastic-dynamic
characteristics. As a result, we derive a process
control method integrating statistical time series
analysis and nonlinear dynamics which captures ~ 80%
(linear R-sq) of the variation in MRR. In this manner
a novel paradigm for effective process control in CMP
has been presented.
of the Chemical Mechanical Planarization (CMP)
process based on combining nonlinear dynamics
principles with statistical process monitoring
approaches. CMP has a direct
bearing on the computational speed and dimensional
characteristics of solid state devices. The challenge
in CMP may be narrowed to domains enveloping
productivity, measured in terms of material removal
rate (MRR), and quality which is usually specified in
terms of surface roughness - Ra, within wafer
non-uniformity (WIWNU), defect rate, etc. In this
work, experimental investigations of CMP are executed
with the aid of sensors. The analysis of the data
reveals the presence of pronounced stochastic-dynamic
characteristics. As a result, we derive a process
control method integrating statistical time series
analysis and nonlinear dynamics which captures ~ 80%
(linear R-sq) of the variation in MRR. In this manner
a novel paradigm for effective process control in CMP
has been presented.
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