A Review Study on ML-based Methods for Defect-Pattern Recognition in Wafer Maps-Reference-Cited by-同舟云学术

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A Review Study on ML-based Methods for Defect-Pattern Recognition in Wafer Maps

Published:2023 Issue: Volume:217 Page:570-583
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Theodosiou T.,Rapti A.,Papageorgiou K.,Tziolas T.,Papageorgiou E.,Dimitriou N.,Margetis G.,Tzovaras D.

Funder

European Union's Research and Innovation

European Commission

Publisher

Elsevier BV

Subject

General Engineering

Reference55 articles.

1. Manufacturing Analytics and Industrial Internet of Things;Lade;IEEE Intell Syst,2017

2. A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests;Wu;Journal of Manufacturing Science and Engineering,2017

3. Enabling Smart Manufacturing Technologies for Decision-Making Support;Helu,2016

4. Machine learning in manufacturing: advantages, challenges, and applications;Wuest;Production & Manufacturing Research,2016

5. Deep learning for smart manufacturing: Methods and applications;Wang;Journal of Manufacturing Systems,2018

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1. Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system;PLOS ONE;2024-08-28

2. Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps;Journal of Intelligent Manufacturing;2024-05-20

3. Accurate and energy efficient ad-hoc neural network for wafer map classification;Journal of Intelligent Manufacturing;2024-05-01

4. Forest fire surveillance systems: A review of deep learning methods;Heliyon;2024-01

5. Enhancing 6 nm CD Patterned Defect Classification with TSOM and CNNs;2023 34th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC);2023-05-01

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