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.
Technical Library | 2023-05-22 16:49:42.0
Our customers' issues • Apertures are getting smaller • Paste does not release as well • Contaminates the bottom of the stencil • Increases defects / reduces yield Insufficient solder Bridging Solder balls on surface of PCB Flux residue • Requires more frequent cleaning • Reduced efficiency (wasted time) • Increased use of consumables (cost) USC fabric (use "cheap" fabric to reduce cost) Lint creates more defects Cleaning chemistries (use IPA to reduce cost) IPA breaks down flux and can create more defects
Technical Library | 2023-08-02 18:18:23.0
As six sigma (6) and better processes are demanded for higher yields and as organizations move from measuring defects in terms of parts-per-million (ppm) towards parts-per-billion (ppb), the resolution of extant control charts is becoming insufficient to monitor process quality. This work describes the development of a new statistical process control (SPC) chart that is used to monitor processes in terms of defects-per-billion-opportunities (dpbo). A logical extension of the defects-per-million-opportunities (dpmo) control chart, calculations used to derive the dpbo control limits will be presented and examples of in-control and out-of-control processes will be offered.
Technical Library | 2016-11-30 21:30:50.0
Mid-chip solder balling is a defect typically associated with solder paste exhibiting poor hot slump and/or insufficient wetting during the reflow soldering process, resulting in paste flowing under the component or onto the solder resist. Once molten, this solder is compressed and forced to the side of the component, causing mid-chip solder balling.This paper documents the experimental work performed to further understand the impact on mid-chip solder balling from both the manufacturing process and the flux chemistry.
Technical Library | 2023-04-17 17:05:47.0
In an ideal world, manufacturing devices would work all of the time, however, every company receives customer returns for a variety of reasons. If these returned parts contributed to a fail, most companies will perform failure analysis (FA) on the returned parts to determine the root cause of the failure. Failure can occur for a multitude of reasons, for example: wear out, fatigue, design issues, manufacturing flaw or defect. This information is then used to improve the overall quality of the product and prevent reoccurrence. If no defect is found, it is possible that in fact the product has no defect. On the other hand, the defect could be elusive and the FA techniques insufficient to detect said deficiency. No-clean flux residues can cause intermittent or elusive, hard to find defects. In an attempt to understand the effects of no-clean flux residues from the secondary soldering and cleaning processes, a matrix of varying process and cleaning operation was investigated. Of special interest, traveling flux residues and entrapped residues were examined, as well as localized and batch cleaning processes. Various techniques were employed to test the remaining residues in order to assess their propensity to cause a latent failure. These techniques include Surface Insulation Resistance1 (SIR) testing at 40⁰C/90% RH, 5 VDC bias along with C32 testing and Ion Exchange Chromatography (IC). These techniques facilitate the assessment of the capillary effect the tight spacing these component structures have when flux residues are present. It is expected that dendritic shorting and measurable current leakage will occur, indicating a failing SIR test. However, since the residue resides under the discrete components, there will be no visual evidence of dendritic growth or metal migration.
Technical Library | 2020-08-27 01:15:10.0
Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers' first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.
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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