  A cross-benchmark has been done on three critical aspects, data imputing,
feature selection and regression algorithms, for machine learning based
chemical vapor deposition (CVD) virtual metrology (VM). The result reveals that
linear feature selection regression algorithm would extensively under-fit the
VM data. Data imputing is also necessary to achieve a higher prediction
accuracy as the data availability is only ~70% when optimal accuracy is
obtained. This work suggests a nonlinear feature selection and regression
algorithm combined with nearest data imputing algorithm would provide a
prediction accuracy as high as 0.7. This would lead to 70% reduced CVD
processing variation, which is believed to will lead to reduced frequency of
physical metrology as well as more reliable mass-produced wafer with improved
quality.
