Technical Library | 2023-11-20 17:30:11.0
Summary for today 1. Electronic component inspection and failure analysis. 2. Component counting and material management. 3. Reverse engineering. 4. Counterfeit detection. 5. Real-time defect verification. 6. Computed tomography (CT) techniques and how to differentiate between 2D, 2.5D, and 3D x-ray inspection. 7. Design for manufacturing (DFM) and design for x-ray inspection (DFXI). 8. Voids, bridging, and head-in-pillow failures in bottom terminated components (BTC). 9. Artificial Intelligence and x-ray inspection
Technical Library | 2012-12-06 17:36:37.0
Inspection of integrated power electronics equals sophisticated test task. X-ray inspection based on 2D / 2.5D principles not utilizable. Full 3D inspection with adapted image capturing and reconstruction is necessary for test task.... First published in the 2012 IPC APEX EXPO technical conference proceedings.
Technical Library | 2010-09-16 18:45:06.0
With PCB complexity and density increasing and also wider use of 3D devices, tougher requirements are now imposed on device inspection both during original manufacture and at their subsequent processing onto printed circuit boards. More complicated and de
Technical Library | 2023-11-20 17:36:58.0
With PCB complexity and density increasing and also wider use of 3D devices, tougher requirements are now imposed on device inspection both during original manufacture and at their subsequent processing onto printed circuit boards. More complicated and dense packages have more opportunities to exhibit defects both internal to the package as well as to the PCB. As components increase in complexity their cost increases, making counterfeiting them a potentially lucrative business for unscrupulous individuals and organizations.
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.
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