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 with guilwing type leads, 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 3-D 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 crosssectional 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(electrically short), adequate solder volume. The volnme of the solder joint is represented by a gray level in the x-ray images : thus solderjoints can be examined by use ofthe gray-level profiles of each joint. To inspect and classify various defects, pattern classification method using a learning vector quantization(LVQ) neural network and a look up table(LUT) is proposed. The clusters into which a gray-level profile is classified are generated by the learning process ofthe network by using a number ofsampled gray-level profiles. A series ofthese 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.

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This paper presents the development of an automatic inspection system to check lens focus status and white balance level and to inspect defects including black and white defect, dim defect, color defect, and line defects in manufacturing process of compact camera module. To check the imaging status and inspect the defects of compact camera module, a unique image capturing system is developed to get image data from CMOS sensor at high speed. It has a complex programmable logic device, and the camera link and the frame grabber is used to transfer and store images to PC. Several kinds of unique image charts are designed to inspect the various types of defects in compact camera module, and they are implemented and displayed on the LCD monitor directly to reduce handling and exchanging time of inspection charts during test procedures. Various image processing algorithms are developed to analyze the captured image from each test chart and to find and verify the defects of camera modules. The experimental results show that the proposed system is able to reliably inspect various types of defects with high precision and high speed in real manufacturing condition.

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