Multifeature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units-Reference-Cited by-同舟云学术

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Multifeature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units

Published:2018-01 Issue:1 Volume:15 Page:145-159
ISSN:1545-5955
Container-title:IEEE Transactions on Automation Science and Engineering
language:
Short-container-title:IEEE Trans. Automat. Sci. Eng.

Author:

Haddad Bashar M.^ORCID,Yang Sen,Karam Lina J.,Ye Jieping,Patel Nital S.,Braun Martin W.

Funder

Intel Corporation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/8856/8246664/07557053.pdf?arnumber=7557053

Cited by 36 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. Surface defect detection method based on generative adversarial networks;International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024);2024-06-13

2. Demystifying Defects: Federated Learning and Explainable AI for Semiconductor Fault Detection;IEEE Access;2024

3. Beetle Swarm With Constrained L$\acute {e}$vy Flight for Image Matching;IEEE Transactions on Automation Science and Engineering;2024

4. A Masked Multi-View Bidirectional Network for Class Extremely Imbalanced Object Detection Under Magnetic Flux Leakage Signals;IEEE Transactions on Automation Science and Engineering;2024

5. A WOA-Stacking Grading Mode Based on Multi-heterogeneous Classifiers Ensemble;Mechanisms and Machine Science;2024

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