Technical Library | 2009-05-21 13:41:05.0
Failure due to board flex cracks persists as the dominant failure mode in multi-layer ceramic capacitors (MLCC). (...) This paper is intended to show the impact of temperature cycling, high-temperature life tests, and multiple bend exposures to the MLCC with this flexible termination.
Technical Library | 2017-07-13 16:16:27.0
Controlled humidity and temperature controlled surface insulation resistance (SIR) measurements of flux covered test vehicles, subject to a direct current (D.C.) bias voltage are recognized by a number of global standards organizations as the preferred method to determine if no clean solder paste and wave soldering flux residues are suitable for reliable electronic assemblies. The IPC, Japanese Industry Standard (JIS), Deutsches Institut fur Normung (DIN) and International Electrical Commission (IEC) all have industry reviewed standards using similar variations of this measurement. (...) This study will compare the results from testing two solder pastes using the IPC-J-STD-004B, IPC TM-650 2.6.3.7 surface insulation resistance test, and IPC TM-650 2.3.25 in an attempt to investigate the correlation of ROSE methods as predictors of electronic assembly electrical reliability.
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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