Proceedings of the 2018 2nd International Conference on Electrical Engineering and Automation (ICEEA 2018)

Assessment of Physics-Based and Data-Driven Models for Material Removal Rate Prediction in Chemical Mechanical Polishing

Authors
Xuan Li, Cheng Wang, Li Zhang, Xinnong Mo, Dewen Zhao, Changkun Li
Corresponding Author
Xuan Li
Available Online March 2018.
DOI
https://doi.org/10.2991/iceea-18.2018.26How to use a DOI?
Keywords
process control; material removal rate; chemical mechanical polishing
Abstract
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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 2nd International Conference on Electrical Engineering and Automation (ICEEA 2018)
Part of series
Advances in Engineering Research
Publication Date
March 2018
ISBN
978-94-6252-497-2
ISSN
2352-5401
DOI
https://doi.org/10.2991/iceea-18.2018.26How to use a DOI?
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
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  -