Machine Learning based CVD Virtual Metrology in Mass Produced Semiconductor Process

Abstract

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.

Advanced process control by machine learning-based virtual metrology for high product mix manufacturing

“…Conventional VM relies on data from the process chamber, known as fault detection and classification (FDC), to predict metrology results. These predictions can then be seamlessly integrated into the process control system, particularly in the context of R2R control [5]. Within this paper, the extraction and application of design features for predictive purposes across varied layouts and technologies are emphasized.…”