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Stacking ensemble learning based material removal rate prediction model for CMP process of semiconductor wafer

Published online by Cambridge University Press:  15 November 2024

Zhilong Song Open the ORCID record for Zhilong Song [Opens in a new window] ,
Wenhong Zhao ,
Xiao Zhang ,
Mingfeng Ke ,
Wei Fang  and
Binghai Lyu
Zhilong Song
Affiliation:
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China Ultra-Precision Machining Center, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou, China
Wenhong Zhao
Affiliation:
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China Ultra-Precision Machining Center, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou, China
Xiao Zhang
Affiliation:
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China Ultra-Precision Machining Center, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou, China
Mingfeng Ke
Affiliation:
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China Ultra-Precision Machining Center, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou, China
Wei Fang
Affiliation:
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China Ultra-Precision Machining Center, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou, China
Binghai Lyu*
Affiliation:
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China Ultra-Precision Machining Center, Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou, China
*
Corresponding author: Lyu Binghai; Email: icewater7812@126.com
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Abstract

The material removal rate (MRR) serves as a crucial indicator in the chemical mechanical polishing (CMP) process of semiconductor wafers. Currently, the mainstream method to ascertain the MRR through offline measurements proves time inefficient and struggles to represent process variability accurately. An efficient MRR prediction model based on stacking ensemble learning that integrates models with disparate architectures was proposed in this study. First, the processing signals collected during wafer polishing, as available in the PHM2016 dataset, were analyzed and preprocessed to extract statistical and neighbor domain features. Subsequently, Pearson correlation coefficient analysis (PCCA) and principal component analysis (PCA) were employed to fuse the extracted features. Ultimately, random forest (RF), light gradient boosting machine (LightGBM), and backpropagation neural network (BPNN) with hyperparameters optimized by the Bayesian Optimization Algorithm were integrated to establish an MRR prediction model based on stacking ensemble learning. The developed model was verified on the PHM2016 benchmark test set, and a Mean Square Error (MSE) of 7.72 and a coefficient of determination (R2) of 95.82% were achieved. This indicates that the stacking ensemble learning based model, integrated with base models of disparate architectures, offers considerable potential for real-time MRR prediction in the CMP process of semiconductor wafers.

Keywords

chemical mechanical polishingmaterial removal rateensemble learningsemiconductor wafer

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Type
Research Article
Information
AI EDAM , Volume 38 , 2024 , e17
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© The Author(s), 2024. Published by Cambridge University Press

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