Assessment of Physics-Based and Data-Driven Models for Material Removal Rate Prediction in Chemical Mechanical Polishing
Xuan Li, Cheng Wang, Li Zhang, Xinnong Mo, Dewen Zhao, Changkun Li
Available Online March 2018.
- https://doi.org/10.2991/iceea-18.2018.26How to use a DOI?
- process control; material removal rate; chemical mechanical polishing
- Material removal rate (MRR) during the chemical mechanical polishing (CMP) process affects the control of product quality. Complexity of various parameters makes it challenging to predict MRR accurately. We addressed this challenge by integrating physics-based modeling with data-driven statistics. First, we analyzed the raw data using data profiling techniques. Then, we extracted features from a physical point of view. Finally, we constructed two Random Forest models respectively based on the feature selection results via the Generic Algorithm. Experiments show that the features we extracted embody key information of each process. The final score predicted by this approach ranked in the second place in a Data Challenge Competition.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xuan Li AU - Cheng Wang AU - Li Zhang AU - Xinnong Mo AU - Dewen Zhao AU - Changkun Li PY - 2018/03 DA - 2018/03 TI - Assessment of Physics-Based and Data-Driven Models for Material Removal Rate Prediction in Chemical Mechanical Polishing BT - 2018 2nd International Conference on Electrical Engineering and Automation (ICEEA 2018) PB - Atlantis Press SP - 116 EP - 121 SN - 2352-5401 UR - https://doi.org/10.2991/iceea-18.2018.26 DO - https://doi.org/10.2991/iceea-18.2018.26 ID - Li2018/03 ER -