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: Chunpu Lv, Jingwei Huang, Ming Zhang, Huangang Wang, Tao Zhang
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Sensors
Subjects:
semi-supervised regression
active learning
deep kernel learning
virtual metrology
phase partition
phase match
Online Access:https://www.mdpi.com/1424-8220/23/9/4392

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