Technical Library | 2018-10-24 18:04:12.0
Polymer Thick Film (PTF)-based printed electronics (aka Printed Electronics) has improved in durability over the last few decades and is now a proven alternative to copper circuitry in many applications once thought beyond the capability of PTF circuitry. This paper describes peak performance and areas for future improvement.State-of-the-art PTF circuitry performance includes the ability to withstand sharp crease tests, 85C/85%RH damp heat 5VDC bias aging (silver migration), auto seat durability cycling, SMT mandrel flexing, and others. The IPC/SGIA subcommittee for Standards Tests development has adopted several ASTM test methods for PTF circuitry and is actively developing needed improvements or additions. These standards are described herein. Advantages of PTF circuitry over copper include: varied conductive material compositions, lower cost and lower environmental impact. Necessary improvements include: robust integration of chip and power, higher conductivity, and fine line multi-layer patterning.
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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