Semi-Supervised Deep Kernel Active Learning for Material Removal Rate Prediction in Chemical Mechanical Planarization

The material removal rate (MRR) is an important variable but difficult to measure in the chemical–mechanical planarization (CMP) process. Most data-based virtual metrology (VM) methods ignore the large number of unlabeled samples, resulting in a waste of information. In this paper, the semi-supervis...

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Bibliographic Details
Main Authors:	Huang, J. (Author), Lv, C. (Author), Wang, H. (Author), Zhang, M. (Author), Zhang, T. (Author)
Format:	Article
Language:	English
Published:	MDPI 2023
Subjects:	active learning Active Learning algorithm article Chemical-mechanical planarization process Clustering algorithms controlled study deep kernel learning Deep kernel learning Deep learning E-learning feature extraction Gaussian distribution Gaussian noise (electronic) Information use Kernel learning learning Learning algorithms Learning systems Material removal rate Mean square error mean squared error phase match Phase match Phase matching phase partition Phase partition physics prediction Principal component analysis Regression analysis regression model Semi-supervised semi-supervised regression Semi-supervised regression virtual metrology Virtual metrology
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Semi-Supervised Deep Kernel Active Learning for Material Removal Rate Prediction in Chemical Mechanical Planarization

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