Chip Pad Inspection Method Based on an Improved YOLOv5 Algorithm
Chip pad inspection is of great practical importance for chip alignment inspection and correction. It is one of the key technologies for automated chip inspection in semiconductor manufacturing. When applying deep learning methods for chip pad inspection, the main problem to be solved is how to ensu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/17/6685 |
| _version_ | 1827664913358651392 |
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| author | Jiangjie Xu Yanli Zou Yufei Tan Zichun Yu |
| author_facet | Jiangjie Xu Yanli Zou Yufei Tan Zichun Yu |
| author_sort | Jiangjie Xu |
| collection | DOAJ |
| description | Chip pad inspection is of great practical importance for chip alignment inspection and correction. It is one of the key technologies for automated chip inspection in semiconductor manufacturing. When applying deep learning methods for chip pad inspection, the main problem to be solved is how to ensure the accuracy of small target pad detection and, at the same time, achieve a lightweight inspection model. The attention mechanism is widely used to improve the accuracy of small target detection by finding the attention region of the network. However, conventional attention mechanisms capture feature information locally, which makes it difficult to effectively improve the detection efficiency of small targets from complex backgrounds in target detection tasks. In this paper, an OCAM (Object Convolution Attention Module) attention module is proposed to build long-range dependencies between channel features and position features by constructing feature contextual relationships to enhance the correlation between features. By adding the OCAM attention module to the feature extraction layer of the YOLOv5 network, the detection performance of chip pads is effectively improved. In addition, a design guideline for the attention layer is proposed in the paper. The attention layer is adjusted by network scaling to avoid network characterization bottlenecks, balance network parameters, and network detection performance, and reduce the hardware device requirements for the improved YOLOv5 network in practical scenarios. Extensive experiments on chip pad datasets, VOC datasets, and COCO datasets show that the approach in this paper is more general and superior to several state-of-the-art methods. |
| first_indexed | 2024-03-10T01:14:16Z |
| format | Article |
| id | doaj.art-7e5fdc73ed0e4f75a160f6628b2bd0f3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:14:16Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7e5fdc73ed0e4f75a160f6628b2bd0f32023-11-23T14:12:44ZengMDPI AGSensors1424-82202022-09-012217668510.3390/s22176685Chip Pad Inspection Method Based on an Improved YOLOv5 AlgorithmJiangjie Xu0Yanli Zou1Yufei Tan2Zichun Yu3School of Electronic and Information Engineering, Guangxi Normal University, Guilin 541000, ChinaSchool of Electronic and Information Engineering, Guangxi Normal University, Guilin 541000, ChinaSchool of Electronic and Information Engineering, Guangxi Normal University, Guilin 541000, ChinaSchool of Electronic and Information Engineering, Guangxi Normal University, Guilin 541000, ChinaChip pad inspection is of great practical importance for chip alignment inspection and correction. It is one of the key technologies for automated chip inspection in semiconductor manufacturing. When applying deep learning methods for chip pad inspection, the main problem to be solved is how to ensure the accuracy of small target pad detection and, at the same time, achieve a lightweight inspection model. The attention mechanism is widely used to improve the accuracy of small target detection by finding the attention region of the network. However, conventional attention mechanisms capture feature information locally, which makes it difficult to effectively improve the detection efficiency of small targets from complex backgrounds in target detection tasks. In this paper, an OCAM (Object Convolution Attention Module) attention module is proposed to build long-range dependencies between channel features and position features by constructing feature contextual relationships to enhance the correlation between features. By adding the OCAM attention module to the feature extraction layer of the YOLOv5 network, the detection performance of chip pads is effectively improved. In addition, a design guideline for the attention layer is proposed in the paper. The attention layer is adjusted by network scaling to avoid network characterization bottlenecks, balance network parameters, and network detection performance, and reduce the hardware device requirements for the improved YOLOv5 network in practical scenarios. Extensive experiments on chip pad datasets, VOC datasets, and COCO datasets show that the approach in this paper is more general and superior to several state-of-the-art methods.https://www.mdpi.com/1424-8220/22/17/6685OCAMattentionYOLOv5chip padsartificial intelligence |
| spellingShingle | Jiangjie Xu Yanli Zou Yufei Tan Zichun Yu Chip Pad Inspection Method Based on an Improved YOLOv5 Algorithm Sensors OCAM attention YOLOv5 chip pads artificial intelligence |
| title | Chip Pad Inspection Method Based on an Improved YOLOv5 Algorithm |
| title_full | Chip Pad Inspection Method Based on an Improved YOLOv5 Algorithm |
| title_fullStr | Chip Pad Inspection Method Based on an Improved YOLOv5 Algorithm |
| title_full_unstemmed | Chip Pad Inspection Method Based on an Improved YOLOv5 Algorithm |
| title_short | Chip Pad Inspection Method Based on an Improved YOLOv5 Algorithm |
| title_sort | chip pad inspection method based on an improved yolov5 algorithm |
| topic | OCAM attention YOLOv5 chip pads artificial intelligence |
| url | https://www.mdpi.com/1424-8220/22/17/6685 |
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