Technical Library: cold joint detection (Page 1 of 2)

PCB vias design recommendation

Technical Library | 2019-05-29 01:47:22.0

1.Vias near SMD pads: Solder can flow into the via after melted. As a result cold joint will appear in the end. Check the picture below. 2.Vias on SMD pads: Solder can flow into the via more easier after melted. Check the picture below. 3.Via opening without soldermask covered. When workers solder TH parts by hand, soldering iron can touch vias sometime, then tiny amounts molten solder will stay on vias. This can lead to electrical short easily. We recommend you make all vias tenting (covered by solder mask) if it is possible.

PCBNPI-Professional PCB Fab/PCB Assembly Service Provider From China

Using Automated 3D X-Ray Inspection to Detect BTC Defects

Technical Library | 2013-07-25 14:02:15.0

Bottom-termination components (BTC), such as QFNs, are becoming more common in PCB assemblies. These components are characterized by hidden solder joints. How are defects on hidden DFN joints detected? Certainly, insufficient solder joints on BTCs cannot be detected by manual visual inspection. Nor can this type of defect be detected by automated optical inspection; the joint is hidden by the component body. Defects such as insufficients are often referred to as "marginal" defects because there is likely enough solder present to make contact between the termination on the bottom-side of the component and the board pad for the component to pass in-circuit and functional test. Should the board be subjected to shock or vibration, however, there is a good chance this solder connection will fracture, leading to an open connection.

Flex (Flextronics International)

Comparison Of Active And Passive Temperature Cycling

Technical Library | 2020-12-10 15:49:40.0

Electronic assemblies should have longer and longer service life. Today there are partially demanded 20 years of functional capability for electronics for automotive application. On the other hand, smaller components, such as resistors of size 0201, are able to endure an increasing number of thermal cycles until fail of solder joints, so these are tested sometimes up to 4000 cycles. But testing until the end of life is essential for the determination of failure rates and the prognosis of reliability. Such tests require a lot of time, but this is often not available in developing of new modules. A further acceleration by higher cycle temperatures is usually not possible, because the materials are already operated at the upper limit of the load. However, the duration can be shortened by the use of liquids for passive tests, which allow faster temperature changes and shorter dwell times because of better heat transfer compared to air. The question is whether such tests lead to comparable results and what failure mechanisms are becoming effective. The same goes for active temperature cycles, in which the components itself are heated from inside and the substrate remains comparatively cold. This paper describes the various accelerated temperature cycling tests, compares and evaluates the related degradation of solder joints.

University of Rostock

Head-in-Pillow BGA Defects

Technical Library | 2009-11-05 11:17:32.0

Head-in-pillow (HiP), also known as ball-and-socket, is a solder joint defect where the solder paste deposit wets the pad, but does not fully wet the ball. This results in a solder joint with enough of a connection to have electrical integrity, but lacking sufficient mechanical strength. Due to the lack of solder joint strength, these components may fail with very little mechanical or thermal stress. This potentially costly defect is not usually detected in functional testing, and only shows up as a failure in the field after the assembly has been exposed to some physical or thermal stress.

AIM Solder

Applying Microscopic Analytic Techniques For Failure Analysis In Electronic Assemblies

Technical Library | 2021-09-21 20:36:45.0

The present paper gives an overview of surface failures, internal nonconformities and solders joint failures detected by microscopic analysis of electronic assemblies. Optical microscopy (stereomicroscopy) and Fourier-Transform- Infrared (FTIR) microscopy is used for documentation and failure localization on electronic samples surface. For internal observable conditions a metallographic cross-section analysis of the sample is required. The aim of this work is to present some internal and external observable nonconformities which frequently appear in electronic assemblies. In order to detect these nonconformities, optical microscopy, cross section analysis, FTIR-microscopy and scanning electron microscopy with energy dispersive spectrometry (SEM-EDS) were used as analytical techniques.

ZOLLNER ELECTRONICS, INC.

Void Detection in Large Solder Joints of Integrated Power Electronics

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.

GOEPEL Electronic

Detection of PCB Soldering Defects using Template Based Image Processing Method

Technical Library | 2021-04-15 14:49:27.0

In this study, a predefined template-based image processing system is proposed to automatically detect of PCB soldering defects that negatively affect circuit operation. The proposed system consists of a scaled inspection structure, a camera, an image processing algorithm merged with Fuzzy and template guided inspection process. The prototype is produced using a plastic material, depending on the focal length of the camera and the PCB size. Image processing step comprises two steps. Firstly, solder joints are determined and boxed using Fuzzy C-means clustering algorithm.

Selcuk University

Enhanced X-Ray Inspection of Solder Joints in SMT Electronics Production using Convolutional Neural Networks

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.

Siemens Process Industries and Drives

Side Wettable Flanks for Leadless Automotive Packaging

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.

Amkor Technology, Inc.

Deep Learning Based Defect Detection for Solder Joints on Industrial X-Ray Circuit Board Images

Technical Library | 2021-05-06 13:41:55.0

Quality control is of vital importance during electronics production. As the methods of producing electronic circuits improve, there is an increasing chance of solder defects during assembling the printed circuit board (PCB). Many technologies have been incorporated for inspecting failed soldering, such as X-ray imaging, optical imaging, and thermal imaging. With some advanced algorithms, the new technologies are expected to control the production quality based on the digital images. However, current algorithms sometimes are not accurate enough to meet the quality control. Specialists are needed to do a follow-up checking. For automated X-ray inspection, joint of interest on the X-ray image is located by region of interest (ROI) and inspected by some algorithms. Some incorrect ROIs deteriorate the inspection algorithm.

Southeast University (SEU)

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