Die-level defects classification using region-based convolutional neural network
Visual inspection process on semiconductors is usually performed by human experts. These inspection tasks require extreme concentration, and the time an inspector could continue the inspection tasks is limited. An automated die-level defects classification system is presented in this paper to replac...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/98693/ http://dx.doi.org/10.1109/ICSE56004.2022.9863135 |
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| Summary: | Visual inspection process on semiconductors is usually performed by human experts. These inspection tasks require extreme concentration, and the time an inspector could continue the inspection tasks is limited. An automated die-level defects classification system is presented in this paper to replace human experts in inspection tasks. The proposed system utilizes a Region-based Convolutional Neural Network on die-level images for defect detection and classification. Four defect classes are considered, blob, die crack, pinhole, and underfill. The proposed method achieved 88.5% and 71.4% accuracy in detection and defect classification, which is equivalent to that performed by human inspectors of between 60 - 80%. |
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