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Predictive modeling of material removal rate in chemical mechanical planarization with physics-informed machine learning

Journal Article


Abstract


  • Chemical mechanical planarization (CMP) is a high-precision and complex manufacturing process that removes material with chemical and mechanical forces in order to achieve highly planar surfaces. Significant research efforts have been devoted to developing physics-based and data-driven approaches to predicting the material removal rate (MRR) in the CMP process. Both physics-based and data-driven methods have advantages and disadvantages. A novel physics-informed machine learning approach is introduced to combine a physics-based model of MRR in CMP with a data-driven model of MRR in CMP. The physics-based model takes into account the contact between a polishing pad and abrasives and the contact between abrasives and a wafer. The data-driven model trained by a machine learning algorithm predicts the asperity radius and asperity density of the polishing pad using the polishing pad wear and conditioner wear estimated by the physics-based model. The predicted asperity radius and asperity density of the polishing pad are then used to estimate MRR in CMP. Experimental data collected from a CMP tool under varying operating conditions are used to train and validate the predictive model. Experimental results have shown that the physics-informed machine learning method is capable of predicting MRR in CMP with high accuracy.

UOW Authors


  •   Li, Zhixiong (external author)

Publication Date


  • 2019

Published In


Citation


  • Yu, T., Li, Z., & Wu, D. (2019). Predictive modeling of material removal rate in chemical mechanical planarization with physics-informed machine learning. Wear, 426-427, 1430-1438. doi:10.1016/j.wear.2019.02.012

Scopus Eid


  • 2-s2.0-85061597348

Start Page


  • 1430

End Page


  • 1438

Volume


  • 426-427

Abstract


  • Chemical mechanical planarization (CMP) is a high-precision and complex manufacturing process that removes material with chemical and mechanical forces in order to achieve highly planar surfaces. Significant research efforts have been devoted to developing physics-based and data-driven approaches to predicting the material removal rate (MRR) in the CMP process. Both physics-based and data-driven methods have advantages and disadvantages. A novel physics-informed machine learning approach is introduced to combine a physics-based model of MRR in CMP with a data-driven model of MRR in CMP. The physics-based model takes into account the contact between a polishing pad and abrasives and the contact between abrasives and a wafer. The data-driven model trained by a machine learning algorithm predicts the asperity radius and asperity density of the polishing pad using the polishing pad wear and conditioner wear estimated by the physics-based model. The predicted asperity radius and asperity density of the polishing pad are then used to estimate MRR in CMP. Experimental data collected from a CMP tool under varying operating conditions are used to train and validate the predictive model. Experimental results have shown that the physics-informed machine learning method is capable of predicting MRR in CMP with high accuracy.

UOW Authors


  •   Li, Zhixiong (external author)

Publication Date


  • 2019

Published In


Citation


  • Yu, T., Li, Z., & Wu, D. (2019). Predictive modeling of material removal rate in chemical mechanical planarization with physics-informed machine learning. Wear, 426-427, 1430-1438. doi:10.1016/j.wear.2019.02.012

Scopus Eid


  • 2-s2.0-85061597348

Start Page


  • 1430

End Page


  • 1438

Volume


  • 426-427