Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model
- DOI
- 10.1155/2022/1829792
- Published
- 2022-09-15
- Container
- Advances in Materials Science and Engineering
- Publisher
- Wiley
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1155/2022/1829792,
title = {Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model},
author = {Rajesh Doss and Jayabrabu Ramakrishnan and S. Kavitha and S. Ramkumar and G. Charlyn Pushpa Latha and Kiran Ramaswamy},
year = {2022},
journal = {Advances in Materials Science and Engineering},
doi = {10.1155/2022/1829792},
url = {https://doi.org/10.1155/2022/1829792}
}RIS
TY - JOUR TI - Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model AU - Rajesh Doss AU - Jayabrabu Ramakrishnan AU - S. Kavitha AU - S. Ramkumar AU - G. Charlyn Pushpa Latha AU - Kiran Ramaswamy PY - 2022 JO - Advances in Materials Science and Engineering DO - 10.1155/2022/1829792 UR - https://doi.org/10.1155/2022/1829792 ER -
APA
Doss, R., Ramakrishnan, J., Kavitha, S., Ramkumar, S., Latha, G. C. P., & Ramaswamy, K. (2022). Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model. Advances in Materials Science and Engineering. https://doi.org/10.1155/2022/1829792
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- crossref · retrieved 2026-09-25T06:22:45.462Z