Evaluation of FCBGA Package Subjected to Four-Point Bend Reliability Test Using Fiber Array Laser Ultrasonic Inspection System

Reddy, Vishnu V. B.; Ume, I. Charles; Mebane, Aaron; Akinade, Kola; Rogers, Bryan; Guirguis, Cherif; Patel, Parimal B.; Derksen, Kathy; Case, R. O.

doi:10.1109/tcpmt.2019.2911073

IEEE Trans. Compon., Packag. Manufact. Technol.

2019

DOI: 10.1109/tcpmt.2019.2911073

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Evaluation of FCBGA Package Subjected to Four-Point Bend Reliability Test Using Fiber Array Laser Ultrasonic Inspection System

Vishnu V. B. Reddy

I. Charles Ume

Aaron Mebane

et al.

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Cited by 6 publications

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Weakly-Supervised Defect Segmentation on Periodic Textures Using CycleGAN

Kim

Ra³

et al. 2020

IEEE Access

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The importance of an automated defect inspection system has been increasing in the manufacturing industries. Various products to be examined have periodic textures. Among image-based inspection systems, it is common that supervised defect segmentation requires a great number of defect images with their own region-level labels; however, it is difficult to prepare sufficient training data. Because most products are of normal quality, it is difficult to obtain images of product defects. Pixelwise annotation for semantic segmentation tasks is an exhausting and time-consuming process. To solve these problems, we propose a weakly-supervised defect segmentation framework for defect images with periodic textures and a data augmentation process using generative adversarial networks. With only imagelevel labeling, the proposed segmentation framework translates a defect image into its defect-free version, called a golden template, using CycleGAN and then segments the defects by comparing the two images. The proposed augmentation process creates whole new synthetic defect images from real defect images to obtain sufficient data. Furthermore, synthetic non-defect images are generated even from real defect images through the augmentation process. The experimental results demonstrate that the proposed framework with data augmentation outperforms an existing weakly-supervised method and shows remarkable results comparable to those of supervised segmentation methods. INDEX TERMS Automated defect inspection, visual inspection system, weakly-supervised learning, periodic textures, data augmentation, generative adversarial networks.

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Weakly-Supervised Defect Segmentation on Periodic Textures Using CycleGAN

Kim

Ra³

et al. 2020

IEEE Access

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Evaluation of the Quality of BGA Solder Balls in FCBGA Packages Subjected to Thermal Cycling Reliability Test Using Laser Ultrasonic Inspection Technique

Reddy

Ume

Williamson

et al. 2021

IEEE Trans. Compon., Packag. Manufact. Technol.

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Highly Stable Pin Pull Test Method for PCB Pad Cratering Characterization

He,

Cai,

Yun

et al. 2024

IEEE Trans. Instrum. Meas.

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Evaluation of FCBGA Package Subjected to Four-Point Bend Reliability Test Using Fiber Array Laser Ultrasonic Inspection System

Cited by 6 publications

References 9 publications

Weakly-Supervised Defect Segmentation on Periodic Textures Using CycleGAN

Weakly-Supervised Defect Segmentation on Periodic Textures Using CycleGAN

Evaluation of the Quality of BGA Solder Balls in FCBGA Packages Subjected to Thermal Cycling Reliability Test Using Laser Ultrasonic Inspection Technique

Highly Stable Pin Pull Test Method for PCB Pad Cratering Characterization

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