Open Access
| Issue |
SHS Web Conf.
Volume 230, 2026
SYMBICON 2026 – 5th Annual International Conference on Sustainability, Innovation, and Technology
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 14 | |
| Section | Digital Transformation, AI and Sustainable Systems | |
| DOI | https://doi.org/10.1051/shsconf/202623003001 | |
| Published online | 10 April 2026 | |
- Kim, Dongil, Pilsung Kang, Sungzoon Cho, Hyoung-joo Lee, and Seungyong Doh. “Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing.” Expert Systems with Applications 39, no. 4 (2022): 4075–4083. [Google Scholar]
- Lee, Hoyeop, Youngju Kim, and Chang Ouk Kim. “A deep learning model for robust wafer fault monitoring with sensor measurement noise.” IEEE Transactions on Semiconductor Manufacturing 30, no. 1 (2021): 23–31. [Google Scholar]
- Mehta, Sourav, and Nadeem Rao. “Enhanced Fault Detection in Semiconductor Wafers Using Multisensor Data Fusion and Machine Learning Techniques.” In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), pp. 631–637. IEEE, 2024. [Google Scholar]
- Singh, Navneet Pratap, Atrija Haldar, Mehul Pathak, and Bhavya Vats. “Wafer Fault detection in manufacturing using Machine Learning.” In 2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI), pp. 1–6. IEEE, 2024. [Google Scholar]
- Cheng, Ken Chau-Cheung, Leon Li-Yang Chen, Ji-Wei Li, Katherine Shu-Min Li, Nova Cheng-Yen Tsai, Sying-Jyan Wang, Andrew Yi-Ann Huang et al. “Machine learning-based detection method for wafer test induced defects.” IEEE Transactions on Semiconductor Manufacturing 34, no. 2 (2021): 161–167. [Google Scholar]
- Jeon, Jeong Eun, Sang Jeen Hong, and Seung-Soo Han. “Condition-Based Monitoring for Failure Detection and Cause Identification in Wafer Transfer Robot Using Machine Learning and Explainable Artificial Intelligence Algorithms.” In 2024 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp. 1–5. IEEE, 2024. [Google Scholar]
- Kim, Tongwha, and Kamran Behdinan. “Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review.” Journal of Intelligent Manufacturing 34, no. 8 (2023): 3215–3247. [Google Scholar]
- Shih, Dong-Her, Cheng-Yu Yang, Ting-Wei Wu, and Ming-Hung Shih. “Investigating a Machine Learning Approach to Predicting White Pixel Defects in Wafers—A Case Study of Wafer Fabrication Plant F.” Sensors 24, no. 10 (2024): 3144. [Google Scholar]
- Taha, Kamal. “Observational and experimental insights into machine learning-based defect classification in wafers.” Journal of Intelligent Manufacturing (2025): 1–51. [Google Scholar]
- Rawat, Savita, Deepak Banerjee, Amit Gupta, and Vijay Singh. “Enhanced Prediction of Semiconductor Wafer Defects through CNN Forest Fusion and Random Methods for Improved Efficiency.” In 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), pp. 1–6. IEEE, 2024. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

