2022 China Semiconductor Technology International Conference (CSTIC) 2022
DOI: 10.1109/cstic55103.2022.9856857
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Based on Deep Learning CD-SEM Image Defect Detection System

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Cited by 3 publications
(3 citation statements)
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“…However, the majority of these past works use SEM image only as input, whereas the current work uses both SEM image and design layout image for more accurate detection. For example, works such as [3,4,5,6] similarly use YOLO but the focus is on finding certain patterns in SEM images without comparing with intended design, which means that if a pattern such as contact is missing, it would not get detected as defect as there is no reference for comparison. Combining SEM image and layout image into a single input as 2 channels has been done in [7] but the work uses Generative Adversarial Network (GAN) model for defect detection and is constrained to only two defect types.…”
Section: Related Workmentioning
confidence: 99%
“…However, the majority of these past works use SEM image only as input, whereas the current work uses both SEM image and design layout image for more accurate detection. For example, works such as [3,4,5,6] similarly use YOLO but the focus is on finding certain patterns in SEM images without comparing with intended design, which means that if a pattern such as contact is missing, it would not get detected as defect as there is no reference for comparison. Combining SEM image and layout image into a single input as 2 channels has been done in [7] but the work uses Generative Adversarial Network (GAN) model for defect detection and is constrained to only two defect types.…”
Section: Related Workmentioning
confidence: 99%
“…They use a classification model to identify the type of defect and generate the heatmap showing class activation maps to localize defect positions. Yan et al 5 proposed a defect inspection system using object detection model. They use a pretrained object detection model and apply transfer learning to reduce the cost of collecting many SEM images and labels.…”
Section: Deep Learning-based Defect Inspectionmentioning
confidence: 99%
“…They use a classification model to identify the type of defect and generate the heatmap showing class activation maps to localize defect positions. Yan et al 5 . proposed a defect inspection system using object detection model.…”
Section: Related Workmentioning
confidence: 99%