Application of Machine Learning-Based Electrochemical Deposition Models to CMP Modeling
DOI:
https://doi.org/10.51408/1963-0051Keywords:
CMP, ECD, Machine learning, Neural networks, LSTM, XGBoost.Abstract
Chemical mechanical polishing/planarization (CMP) is the primary process used for modern integrated circuits (IC) manufacturing. Modeling of the post-CMP surface profile is critical for detecting planarity hotspots prior to manufacturing and avoiding fatal failures of chips. Electrochemical deposition (ECD) is a key process for the void-free filling of interconnection wires and vias in modern chips. Large surface topography variations generated after ECD affect the post-CMP surface profile. In this paper, several machine learning approaches are used to model surface profiles after ECD that are used as input to CMP models. Different combinations of deep neural networks, long-shortterm-memory (LSTM) recurrent networks, convolutional neural networks and XGBoost algorithms are investigated and compared. The model based on the XGBoost library showed superior performance and accuracy
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