지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE Journal of Semiconductor Technology and Science Vol.23 No.5
- 발행연도
- 2023.10
- 수록면
- 251 - 257 (7page)
- DOI
- 10.5573/JSTS.2023.23.5.251
이용수
초록· 키워드
오류제보하기
At SK hynix Wafer Level Package (WLPKG) line, there are plenty of measuring and inspection steps to ensure the quality of High Bandwidth Memory (HBM). Although most of the measuring and inspection steps are handled automatically, some of the steps still need confirmation from line operators with their naked eye and skills. Since the operators" skills are different, sometimes it causes human errors, and these risks become chronic problems for the company. To solve this problem, Package & Test (P&T) group at SK hynix has been steadily promoting the inspection automation system using deep learning. However, deep learning has the disadvantage of not providing interpretation information, such as which area is actually defective in the target image and its shape for the ‘Excellent’ result of outputs. In this paper, we will introduce cases in which defect patterns are automatically extracted from inspection images taken during the SiN / SiO2 film deposition process by using two deep-learning segmentation models. The performance of the technology to be introduced was demonstrated by comparing the Mean IoU value between the extracted defect image and label mask. Through the proposed technology, we intend to contribute to unmanned inspection verification tasks in the future and accelerate the realization of Industry 4.0.
목차
Abstract
I. INTRODUCTION
II. RELATED RESEARCH: DEEP LEARNING SEGMENTATION MODELS
III. EXPERIMENT
IV. EXPERIMENT RESULT
V. CONCLUSION
REFERENCE
참고문헌 (9)
참고문헌 신청
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Selvaraju, Ramprasaath R., et al. “Grad-Cam: Visual Explanations from Deep Networks via Gradient-Based Localization.” 2017 IEEE International Conference on Computer Vision (ICCV), 2017, https://doi.org/10.1109/iccv.2017.74.
Dong, Xinghui, et al. “Small Defect Detection Using Convolutional Neural Network Features and Random Forests.” Lecture Notes in Computer Science, Sept. 2019, pp. 398-412, https://doi.org/10.1007/978-3-030-11018-5_35.
Zhou, Bolei, et al. “Learning Deep Features for Discriminative Localization.” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, https://doi.org/10.1109/cvpr.2016.319.
Batool, Uzma, et al. “A Systematic Review of Deep Learning for Silicon Wafer Defect Recognition.” IEEE Access, vol. 9, 2021, pp. 116572-116593, https://doi.org/10.1109/access.2021.3106171.
Selvaraju, Ramprasaath R., et al. “Grad-Cam: Visual Explanations from Deep Networks via Gradient-Based Localization.” 2017 IEEE International Conference on Computer Vision (ICCV), 2017, https://doi.org/10.1109/iccv.2017.74.
Dong, Xinghui, et al. “Small Defect Detection Using Convolutional Neural Network Features and Random Forests.” Lecture Notes in Computer Science, Sept. 2019, pp. 398-412, https://doi.org/10.1007/978-3-030-11018-5_35.
Zhou, Bolei, et al. “Learning Deep Features for Discriminative Localization.” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, https://doi.org/10.1109/cvpr.2016.319.
Batool, Uzma, et al. “A Systematic Review of Deep Learning for Silicon Wafer Defect Recognition.” IEEE Access, vol. 9, 2021, pp. 116572-116593, https://doi.org/10.1109/access.2021.3106171.
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