[논문]Ball Grid Array Solder Void Inspection Using Mask R-CNN

Kim, Seung Cheol; Jeon, Ho Jeong; Hong, Sang Jeen

Ball Grid Array Solder Void Inspection Using Mask R-CNN 원문보기

반도체디스플레이기술학회지 = Journal of the semiconductor & display technology, v.20 no.2, 2021년, pp.126 - 130

Kim, Seung Cheol (Department of Electronics Engineering & LINC+ Semiconductor Equipment Engineering Program, Myongji University) , Jeon, Ho Jeong (Department of Electronics Engineering & LINC+ Semiconductor Equipment Engineering Program, Myongji University) , Hong, Sang Jeen (Department of Electronics Engineering & LINC+ Semiconductor Equipment Engineering Program, Myongji University)

Abstract ▼ AI-Helper

The ball grid array is one of the packaging methods that used in high density printed circuit board. Solder void defects caused by voids in the solder ball during the BGA process do not directly affect the reliability of the product, but it may accelerate the aging of the device on the PCB layer or interface surface depending on its size or location. Void inspection is important because it is related in yields with products. The most important process in the optical inspection of solder void is the segmentation process of solder and void. However, there are several segmentation algorithms for the vision inspection, it is impossible to inspect all of images ideally. When X-Ray images with poor contrast and high level of noise become difficult to perform image processing for vision inspection in terms of software programming. This paper suggests the solution to deal with the suggested problem by means of using Mask R-CNN instead of digital image processing algorithm. Mask R-CNN model can be trained with images pre-processed to increase contrast or alleviate noises. With this process, it provides more efficient system about complex object segmentation than conventional system.

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Ball Grid Array Solder Void Inspection Using Mask R-CNN 원문보기

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