Automatic optical inspection system for IC solder joint based on local-to-global ensemble learning

Chen, Wenjie; Cai, Nian; Wang, Huiheng; Lin, Jianfa; Wang, Han

doi:10.1108/ssmt-03-2020-0011

Cited by 2 publications

(4 citation statements)

References 28 publications

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“…To validate the proposed method, several state-of-the-art inspection methods were used for comparisons, including GMM-based (Cai et al , 2016), ViBe-based (Cai et al , 2015), Adaptive-template (Ye et al , 2018), Song et al (2019), Wu and Xu (2018) and Chen et al (2020). For fair comparisons, 400 qualified samples were used to train the models for these methods.…”

Section: Resultsmentioning

confidence: 99%

“…As the local-to-global ensemble learning method (Chen et al , 2020) can simultaneously grasp the local characteristics of IC solder joints via ViBe and reveal inherently global relationships between IC solder joints via ensemble learning, it can well characterize the appearances of qualified samples different from those of unqualified ones. However, an imbalanced data problem emerges in ensemble learning.…”

Section: Resultsmentioning

confidence: 99%

“…Error rate Omission rate GMM (Cai et al, 2016) 10.50 1.28 ViBe (Cai et al, 2015) 1.14 2.56 Adaptive-template (Ye et al, 2018) 0.30 0.54 Wu (Wu and Xu, 2018) 8.36 10.25 Song (Song et al, 2019) 6.41 12.17 Chen (Chen et al, 2020) 0.09 11.50 Ours 0.19 0 of IC solder joints via ViBe and reveal inherently global relationships between IC solder joints via ensemble learning, it can well characterize the appearances of qualified samples different from those of unqualified ones. However, an imbalanced data problem emerges in ensemble learning.…”

Section: Methodsmentioning

confidence: 99%

“…Then, the defect degree based on a frequency map was defined to evaluate the qualities of the solder joints. Chen et al (2020) proposed a local-to-global ensemble learning method for IC solder joint inspection. Six templates were established by the improved ViBe algorithm (Cai et al , 2015) to achieve the potential defect image (PDI) for an IC solder joint image.…”

Section: Introductionmentioning

confidence: 99%

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IC solder joint inspection via adaptive statistical modeling

Xiao

Cai

et al. 2022

SSMT

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Purpose Statistical modeling has been successfully applied to integrated circuit (IC) solder joint inspection. However, there are some inherent problems in previous statistical modeling methods. This paper aims to propose an adaptive statistical modeling method to further improve the inspection performance for IC solder joints. Design/methodology/approach First, different pixels in the IC solder joint image were modeled by different templates, each of which was composed of the hue value of the pixel and a proposed template significance factor. Then, the potential defect image was obtained by adaptive template matching and the potential defect threshold for each pixel. It was noted that the number of templates, matching distance threshold, potential defect threshold and updating rate were adaptively updated during model training. Finally, the trained statistical model was used to inspect the IC solder joints by means of defect degree. Findings Experimental results indicated that the proposed adaptive schemes greatly contributed to the inspection performance of statistical modeling. Also, the proposed inspection method achieved better performance compared with some state-of-the-art inspection methods. Originality/value The proposed method offers a promising approach for IC solder joint inspection, which establishes different numbers of templates constructed by pixel values and template significance factors for different pixels. Also, some important parameters were adaptively updated with the updating of the model, which contributed to the inspection performance of the model.

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