Paper
27 August 1999 Inspection of ball grid array (BGA) solder joints using x-ray cross-sectional images
Young Jun Roh, Kuk Won Ko, Hyungsuck Cho, Hyung Cheol Kim, Hyonam Joo, Sung Kwon Kim
Author Affiliations +
Abstract
The ball grid array (BGA) chip is widely used in high density printed circuit board (PCB). However, inspection of defects in the solder joints is difficult by visual or a normal x-ray imaging method, because unlike conventional packages, solder joints of the BGA are located underneath its own package and ball type leads. Therefore, x-ray digital tomosynthesis (DT), which form a cross-sectional image of 3D objects, is needed to image and inspect the solder joints of BGA. In this paper, we propose a series of algorithms for inspecting the solder joints of BGA by using x-ray cross-sectional images that are acquired from the developed DT system. BGA solder joints are examined to check the alignment between the chip and pad on a PCB, bridge, adequate solder volume. The volume of the solder joint is represented by a gray level in the x-ray images: thus solder joints can be examined by use of the gray-level profiles of each joint. To inspect and classify various defects, pattern classification method using a learning vector quantization neural network and a look up table is proposed. The clusters into which a gray-level profile is classified are generated by the learning process of the network by using a number of sampled gray-level profiles. A series of these developed algorithms for inspecting and classifying defects were tested on a number of BGA solder joints. The experimental results show that the proposed method yields satisfactory solutions for inspection based on x-ray cross-sectional images.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Young Jun Roh, Kuk Won Ko, Hyungsuck Cho, Hyung Cheol Kim, Hyonam Joo, and Sung Kwon Kim "Inspection of ball grid array (BGA) solder joints using x-ray cross-sectional images", Proc. SPIE 3836, Machine Vision Systems for Inspection and Metrology VIII, (27 August 1999); https://doi.org/10.1117/12.360270
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CITATIONS
Cited by 17 scholarly publications and 1 patent.
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KEYWORDS
Inspection

X-ray imaging

X-rays

Neural networks

Prototyping

Algorithm development

Neurons

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