A Copula network deconvolution-based direct correlation disentangling framework for explainable fault detection in semiconductor wafer fabrication-Reference-Cited by-同舟云学术

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A Copula network deconvolution-based direct correlation disentangling framework for explainable fault detection in semiconductor wafer fabrication

Published:2024-01 Issue: Volume:59 Page:102272
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

Xu Hong-Wei,Qin Wei,Hu Jin-Hua,Sun Yan-Ning,Lv You-Long,Zhang Jie

Funder

Science and Technology Commission of Shanghai Municipality

National Defense Basic Scientific Research Program of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems,Building and Construction

Reference53 articles.

1. An ANN approach for modeling the multisource yield learning process with semiconductor manufacturing as an example;Chen;Comput. Ind. Eng.,2017

2. Forecasting the yield of wafer by using improved genetic algorithm, high dimensional alternating feature selection and SVM with uneven distribution and high-dimensional data;Xu;Autonomous Intelligent Systems,2022

3. A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges;Li;Mech. Syst. Sig. Process.,2022

4. Data-driven early fault diagnostic methodology of permanent magnet synchronous motor;Cai;Expert Syst. Appl.,2021

5. A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks;Cai;Mech. Syst. Sig. Process.,2016

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A fast ramp-up framework for wafer yield improvement in semiconductor manufacturing systems;Journal of Manufacturing Systems;2024-10

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