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

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
Online Access:View Fulltext in Publisher
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