Ball-Grid-Array Chip Defects Detection and Classification Using Patch-based Modified YOLOv3

Phong Phu Le, Shu Mei Guo, Ju Chin Chen, Jenn Jier James Lien

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The tiny size of defects and the noise information in the Ball-Grid-Array (BGA) Chip images have been challenging the image-processing based visual inspection systems in the Integrated Circuit (IC) manufacturing industry. Moreover, the gradient vanishing and high time-consuming problems of deep neural network models are a big obstacle for its application to solve industrial projects. This paper focuses on proposing a modified version of the YOLOv3 model, a leading object detection and classification method in terms of speed; and its application to deal with the problem of detecting and classifying defects on BGA Chip Images. There are five modifications constructed on (4) YOLOv3 architecture that are aimed to enhance the ability of feature extraction towards small objects, strengthen the flow of information inside the network and eliminate the problem of redundant information. With the application of this model into BGA Chip Defects estimation, 49 patches (320x320 pixels), extracted from a single high-resolution BGA Chip image (1, 450x1,450 pixels), are continuously fed into the modified YOLOv3 model to detect and classify the inner defects. As a result, the problem of BGA Chip Defects estimation is solved with the highest performance achieves an average precision of 86% at IoU (Intersection over Union) of 0.75 and an average recall of 99%.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728146669
DOIs
Publication statusPublished - 2019 Nov
Event24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 - Kaohsiung, Taiwan
Duration: 2019 Nov 212019 Nov 23

Publication series

NameProceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019

Conference

Conference24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
CountryTaiwan
CityKaohsiung
Period19-11-2119-11-23

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction

Cite this

Le, P. P., Guo, S. M., Chen, J. C., & Lien, J. J. J. (2019). Ball-Grid-Array Chip Defects Detection and Classification Using Patch-based Modified YOLOv3. In Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 [8959827] (Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TAAI48200.2019.8959827