Prakash, PKS and McLoone, Sean
Plasma Etch Process Virtual Metrology using
Aggregative Linear Regression.
In: Third International Conference of Soft Computing and Pattern Recognition (SocPaR 2011), 14-16 October 2011, Dalian, China.
To enhance product quality semiconductor
manufacturing industries are increasing the amount of metrology
information collected during manufacturing processes. This
increase in information has provided companies with many
opportunities for enhanced process monitoring and control.
However, the increase in information also posses challenges as it
is quite common now to collect many more measurements than
samples from a process leading to ill-conditioned datasets. Illconditioned
datasets are very common in semiconductor
manufacturing industries where infrequent sampling is the norm.
It is therefore critical to be able to quantify virtual metrology
models developed from such data sets. This paper presents an
aggregative linear regression methodology for modeling that
allows the generation of confidence intervals on the predicted
outputs. The aggregation enhances the robustness of the linear
models in terms of process variation and model sensitivity
towards prediction. Also, to deal with the large number of
candidate process variables, variable selection methods are
employed to reduce the dimensionality and computational efforts
associated with building virtual metrology models. In the paper
three methods for variable selection are evaluated in conjunction
with aggregative linear regression (ALR). The proposed
methodology is tested on a benchmark semiconductor plasma
etch process dataset and the results are compared with state-ofart
multiple linear regression (MLR) and Gaussian Process
Regression (GPR) VM models.
Conference or Workshop Item
||Virtual metrology; Aggregative Linear Regression;
Forward Stepwise Regression; Decision Trees;
||Science & Engineering > Electronic Engineering
||08 May 2012 15:07
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