Technical Library | 2023-11-20 18:10:20.0
The electronics production is prone to a multitude of possible failures along the production process. Therefore, the manufacturing process of surface-mounted electronics devices (SMD) includes visual quality inspection processes for defect detection. The detection of certain error patterns like solder voids and head in pillow defects require radioscopic inspection. These high-end inspection machines, like the X-ray inspection, rely on static checking routines, programmed manually by the expert user of the machine, to verify the quality. The utilization of the implicit knowledge of domain expert(s), based on soldering guidelines, allows the evaluation of the quality. The distinctive dependence on the individual qualification significantly influences false call rates of the inbuilt computer vision routines. In this contribution, we present a novel framework for the automatic solder joint classification based on Convolutional Neural Networks (CNN), flexibly reclassifying insufficient X-ray inspection results. We utilize existing deep learning network architectures for a region of interest detection on 2D grayscale images. The comparison with product-related meta-data ensures the presence of relevant areas and results in a subsequent classification based on a CNN. Subsequent data augmentation ensures sufficient input features. The results indicate a significant reduction of the false call rate compared to commercial X-ray machines, combined with reduced product-related optimization iterations.
Technical Library | 2015-04-02 20:12:58.0
The demands on volume delivery and positioning accuracy for solder paste deposits are increasing as the size and complexity of circuits continue to develop in the electronics industry. According to the iNEMI 2013 placement accuracy for these kinds of components will reach 6 sigma placement accuracy in X and Y of 30 um by 2023.This study attempts to understand the dependencies on piezo actuation pulse profile on jetting deposit quality, especially focused on positioning, satellites and shape. The correlation of deposit diameter and positioning deviation as a function of piezo actuation profile shows that positioning error for deposits increase almost monotonically with decreasing droplet volume irrespective of the piezo-actuation profile. The trends for shape and satellite levels are not as clear and demand further study.
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