Redefining Monitoring Rules for Intelligent Fault Detection and Classification via CNN Transfer Learning for Smart Manufacturing-Reference-Cited by-同舟云学术

Redefining Monitoring Rules for Intelligent Fault Detection and Classification via CNN Transfer Learning for Smart Manufacturing

Published:2022-05 Issue:2 Volume:35 Page:158-165
ISSN:0894-6507
Container-title:IEEE Transactions on Semiconductor Manufacturing
language:
Short-container-title:IEEE Trans. Semicond. Manufact.

Author:

Chien Chen-Fu¹^ORCID,Hung Wei-Tse²^ORCID,Liao Eddy Ting-Yi¹^ORCID

Affiliation:

1. Ministry of Science and Technology and the Department of Industrial Engineering and Engineering Management, Artificial Intelligence for Intelligent Manufacturing Systems Research Center, National Tsing Hua University, Hsinchu, Taiwan

2. Artificial Intelligence for Intelligent Manufacturing Systems Research Center, Ministry of Science and Technology, and the International Intercollegiate Ph.D. Program, National Tsing Hua University, Hsinchu, Taiwan

Funder

Ministry of Science and Technology, Taiwan

Micron Foundation, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/66/9768876/09749610.pdf?arnumber=9749610

Reference35 articles.

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2. Deep Learning-Based Domain Adaptation Method for Fault Diagnosis in Semiconductor Manufacturing

3. Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing

4. Fault Detection and Diagnosis Using Self-Attentive Convolutional Neural Networks for Variable-Length Sensor Data in Semiconductor Manufacturing

5. Agent-based approach integrating deep reinforcement learning and hybrid genetic algorithm for dynamic scheduling for Industry 3.5 smart production

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