In semiconductor fabrication processes, reliable feature extraction and condition monitoring is critical to understanding equipment degradation and implementing the proper maintenance decisions. This paper presents an integrated feature extraction and equipment monitoring approach based on standard built-in sensors from a modern 300mm-technology industrial Plasma Enhanced Chemical Vapor Deposition (PECVD) tool. Linear Discriminant Analysis was utilized to determine the set of dynamic features that are the most sensitive to different tool conditions brought about by chamber cleaning. Gaussian Mixture Models of the dynamic feature distributions were used to statistically quantify changes of these features as the condition of the tool changed. Data was collected in the facilities of a well-known microelectronics manufacturer from a PECVD tool used for depositing various thin films on silicon wafers, which is one of the key steps in semiconductor manufacturing. Dynamic features coming from the radio frequency (RF) plasma power generator, matching capacitors, pedestal temperature, and chamber temperature sensors were shown to consistently have significant statistical changes as a consequence of repeated cleaning cycles, indicating physical connections to the chamber condition.

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