The semiconductor industry is continuously facing four main challenges in film characterization techniques: accuracy, speed, throughput and flexibility. Virtual Metrology (VM), defined as the prediction of metrology variables using process and wafer state information, is able to successfully address these four challenges. VM is understood as definition and application of predictive and corrective mathematical models to specify metrology outputs (physical measurements). These statistical models are based on metrology data and equipment parameters. The objective of this study is to develop a model predicting the CVD oxide thickness (average) for an IMD (Inter Metal Dielectric) deposition process using FDC data (Fault Detection and Classification) and metrology data. In this paper, two VM models are studied: one based on Partial Least Squares Regression (PLS) and one based on Tree ensembles. We will demonstrate that both models show good predictive strength. Finally, we will highlight that model update is key for ensuring a good model robustness over time and that an indicator of confidence of the predicted values is necessary too if the VM model has to be use on-line in a production environment.

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