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A cross-benchmark has been done on three critical aspects, data imputing,feature selection and regression algorithms, for machine learning basedchemical vapor deposition (CVD) virtual metrology (VM). The result reveals thatlinear feature selection regression algorithm would extensively under-fit theVM data. Data imputing is also necessary to achieve a higher predictionaccuracy as the data availability is only ~70% when optimal accuracy isobtained. This work suggests a nonlinear feature selection and regressionalgorithm combined with nearest data imputing algorithm would provide aprediction accuracy as high as 0.7. This would lead to 70% reduced CVDprocessing variation, which is believed to will lead to reduced frequency ofphysical metrology as well as more reliable mass-produced wafer with improvedquality.

Machine Learning based CVD Virtual Metrology in Mass Produced Semiconductor Process