Virtual Metrology for Etch Profile in Silicon Trench Etching With SF6/O2/Ar Plasma

This study practiced virtual metrology (VM) for the etch profile and depth in the deep silicon trench etching with SF6/O-2/Ar plasma. Machine learning-based VM models constitute the classification models of etch profile and the prediction models of etch depth from the silicon trench etch. Machine learning algorithms of random forest and Xgboost were used for classifying etch profiles by employing recipe-based equipment status variable identification (SVID) data. Predictive etch depth models constructed with neural network models employed both equipment SVID data and optical emission spectroscopy (OES) data, which provide chemistry information of the plasma during the etch process. Plasma VM model, augmenting OES data to SVID data presented improved accuracy in predicting etch profile. The augmented phenomenological plasma information during the etch process helped to establish a more accurate VM model in the plasma process. The importance of variables was identified through the permutation importance of each model. Additionally, the actual process results of the variables with high importance were analyzed with the etching reaction of SF6/O-2/Ar plasma.

Automated summary

In this study, virtual metrology (VM) for etch profile and depth in deep silicon trench etching with SF6/O-2/Ar plasma was practiced using machine learning techniques. Classification and prediction models were built based on recipe and optical emission spectroscopy (OES) data, and it was found that the augmented VM model showed improved accuracy in predicting etch profile.