Technical Library | 2023-09-15 11:16:39.0
Elevate your electronics manufacturing with the SMT Offline X-Ray Machine. Achieve comprehensive component inspection and reliability assurance through advanced X-ray technology.
Technical Library | 2023-09-15 11:15:49.0
Experience unmatched component inspection precision with our SMT Offline X-Ray solution. Detect defects, ensure quality, and boost production efficiency with advanced X-ray technology.
Technical Library | 2023-09-15 11:17:10.0
Ensure the integrity of your components with our SMT Offline X-Ray System. Detect hidden defects and improve the quality of your PCBAs with precise X-ray inspection technology.
Technical Library | 2023-09-15 11:14:33.0
Optimize your inventory management with our SMD SMT X-Ray Offline Component Counting Machine. Ensure precise and efficient component counting with advanced X-ray technology. Enhance your operational efficiency and inventory accuracy for SMD and SMT components. Explore how this machine can revolutionize your component counting process and streamline your operations.
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 | 2023-08-04 15:38:36.0
The MicroLeadFrame® (MLF®)/Quad Flat No-Lead (QFN) packaging solution is extremely popular in the semiconductor industry. It is used in applications ranging from consumer electronics and communications to those requiring high reliability performance, such as the automotive industry. The wide acceptance of this packaging design is primarily due to its flexible form factors, size, scalability and thermal dissipation capabilities. The adaptation and acceptance of MLF/QFN packages in automotive high reliability applications has led to the development of materials and processes that have extended its capabilities to meet the performance and quality requirements. One of process developments that is enabling the success of the MLF/QFN within the automotive industry has been the innovation of side wettable flanks that provide the capability to inspect the package lead to printed circuit board (PCB) interfaces for reliable solder joints. Traditionally, through-board X-ray was the accepted method for detecting reliable solder joints for leadless packages. However, as PBC layer counts and routing complexities have increased, this method to detect well-formed solder fillets has proven ineffective and incapable of meeting the inspection requirements. To support increased reliability and more accurate inspection of the leadless package solder joints, processes to form side-wettable flanks have been developed. These processes enable the formation of solder fillets that are detectable using state-of-the-art automated optical inspection (AOI) equipment, providing increased throughput for the surface mount technology (SMT) processes and improved quality as well.
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