Technical Library | 2015-08-27 15:32:16.0
Ever since there has been a widespread usage of surface mount parts, the trend of continued shrinkage of devices with ever finer pitches has continued to challenge PCB assemblers for the rework of same. Todays' pitches are commonly 0.5 to 0.4mm with packages of tiny outline sizes, 5 -10mm square, making the rework of such devices a challenge. In addition to the handling and inspection challenges comes the board density. Spacing to neighboring components continues to be compressed so the rework techniques should not damage neighboring components.
Technical Library | 2016-04-28 14:43:23.0
Underfilling is a long-standing process issued from the micro-electronics that can enhance the robustness and the reliability of first or second-level interconnects for a variety of electronic applications. Its usage is currently spreading across the industry fueled by the decreasing reliability margins induced by the miniaturization and interconnect pitch reduction. (...) This paper will address the control of surface mount under filled assemblies, focusing on applicable inspection techniques and possible options to overcome their limitations.
Technical Library | 2021-05-06 13:45:49.0
The high-sensitive micro eddy-current testing (ECT) probe composed of planar meander coil as an exciter and spin-valve giant magneto-resistance (SV-GMR) sensor as a magnetic sensor for bare printed circuit board (PCB) inspection is proposed in this paper. The high-sensitive micro ECT probe detects the magnetic field distribution on the bare PCB and the image processing technique analyzes output signal achieved from the ECT probe to exhibit and to identify the defects occurred on the PCB conductor. The inspection results of the bare PCB model show that the proposed ECT probe with the image processing technique can be applied to bare PCB inspection. Furthermore, the signal variations are investigated to prove the possibility of applying the proposed ECT probe to inspect the high-density PCB that PCB conductor width and gap are less than 100 μm.
Technical Library | 2017-06-22 17:11:53.0
C-mode scanning acoustic microscopy (C-SAM) is a non-destructive inspection technique showing the internal features of a specimen by ultrasound. The C-SAM is the preferred method for finding “air gaps” such as delamination, cracks, voids, and porosity. This paper presents evaluations performed on various advanced packages/assemblies especially flip-chip die version of ball grid array/column grid array (BGA/CGA) using C-SAM equipment. For comparison, representative x-ray images of the assemblies were also gathered to show key defect detection features of the two non-destructive techniques.
Technical Library | 2012-08-30 21:24:29.0
This paper provides definitions of the different voiding types encountered in Gull Wing solder joint geometries. It further provides corresponding reliability data that support some level of inclusion voiding in these solder joints and identifies the final criteria being applied for certain IBM Server applications. Such acceptance criteria can be applied using various available x-ray inspection techniques on a production or sample basis. The bulk of supporting data to date has been gathered through RoHS server exempt SnPb eutectic soldering operations but it is expected to provide a reasonable baseline for pending Pb-free solder applications.
Technical Library | 2022-06-27 16:50:26.0
Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.
Technical Library | 2021-03-18 20:03:27.0
Much has been said and written about the accuracy of visual attribute inspections of potentially counterfeit components. The techniques and procedures being used to inspect counterfeit and reworked electronic components in the open marketplace can be quite effective in most cases.
Technical Library | 2021-09-15 18:44:20.0
Analyzing failures is a critical process in determining the physical root causes of problems. The process is complex, draws upon many different technical disciplines, and uses a variety of observation, inspection, and laboratory techniques. One of the key factors in properly performing a failure analysis is keeping an open mind while examining and analyzing the evidence to foster a clear, unbiased perspective of the failure.
Technical Library | 2021-11-22 20:44:44.0
Many automated optical inspection (AOI) companies use supervised object detection networks to inspect items, a technique which expends tremendous time and energy to mark defectives. Therefore, we propose an AOI system which uses an unsupervised learning network as the base algorithm to simultaneously generate anomaly alerts and reduce labeling costs. This AOI system works by deploying the GANomaly neural network and the supervised network to the manufacturing system. To improve the ability to distinguish anomaly items from normal items in industry and enhance the overall performance of the manufacturing process, the system uses the structural similarity index (SSIM) as part of the loss function as well as the scoring parameters. Thus, the proposed system will achieve the requirements of smart factories in the future (Industry 4.0).
Technical Library | 2023-11-20 17:30:11.0
Summary for today 1. Electronic component inspection and failure analysis. 2. Component counting and material management. 3. Reverse engineering. 4. Counterfeit detection. 5. Real-time defect verification. 6. Computed tomography (CT) techniques and how to differentiate between 2D, 2.5D, and 3D x-ray inspection. 7. Design for manufacturing (DFM) and design for x-ray inspection (DFXI). 8. Voids, bridging, and head-in-pillow failures in bottom terminated components (BTC). 9. Artificial Intelligence and x-ray inspection
