Full-Wafer Voltage Contrast Inspection for Detection of BEOL Defects

Hafer, Richard F.; Patterson, Oliver D.; Hahn, Roland; Xiao, H.

doi:10.1109/tsm.2015.2477941

Cited by 18 publications

(2 citation statements)

References 6 publications

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“…Wafer surface defect detection has gone through three generations: (1) image processingbased algorithms that align the template image with the wafer image and highlight the defect area by difference operation [2][3][4][5]; (2) machine learning (ML)-based algorithms that utilize the machine learning algorithm to classify the defect area [6][7][8]; (3) deep learningbased algorithms that apply a deep convolutional neural network for classification and localization [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Wafer Surface Defect Detection Based on Background Subtraction and Faster R-CNN

Zheng

Zhang

2023

Micromachines

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Concerning the problem that wafer surface defects are easily confused with the background and are difficult to detect, a new detection method for wafer surface defects based on background subtraction and Faster R-CNN is proposed. First, an improved spectral analysis method is proposed to measure the period of the image, and the substructure image can then be obtained on the basis of the period. Then, a local template matching method is adopted to position the substructure image, thereby reconstructing the background image. Then, the interference of the background can be eliminated by an image difference operation. Finally, the difference image is input into an improved Faster R-CNN network for detection. The proposed method has been validated on a self-developed wafer dataset and compared with other detectors. The experimental results show that compared with the original Faster R-CNN, the proposed method increases the mAP effectively by 5.2%, which can meet the requirements of intelligent manufacturing and high detection accuracy.

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Section: Introductionmentioning

confidence: 99%

Wafer Surface Defect Detection Based on Background Subtraction and Faster R-CNN

Zheng

Zhang

2023

Micromachines

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“…Early wafer surface defect detection methods were mainly based on image processing technology [4][5][6][7]. In these methods, through the difference between a template image without defects and an image to be tested, each defect area is obtained using the threshold segmentation method, and the texture and shape features of the defect areas are extracted.…”

Section: Introductionmentioning

confidence: 99%

Wafer Surface Defect Detection Based on Feature Enhancement and Predicted Box Aggregation

Zheng

Dang²,

Zhang

2022

Electronics

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For wafer surface defect detection, a new method based on improved Faster RCNN is proposed here to solve the problems of missing detection due to small objects and multiple boxes detection due to discontinuous objects. First, focusing on the problem of small objects missing detection, a feature enhancement module (FEM) based on dynamic convolution is proposed to extract high-frequency image features, enrich the semantic information of shallow feature maps, and improve detection performance for small-scale defects. Second, for the multiple boxes detection caused by discontinuous objects, a predicted box aggregation method is proposed to aggregate redundant predicted boxes and fine-tune real predicted boxes to further improve positioning accuracy. Experimental results show that the mean average precision of the proposed method, when validated on a self-developed dataset, reached 87.5%, and the detection speed was 0.26 s per image. The proposed method has a certain engineering application value.

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