Technical Library | 2013-08-15 13:12:11.0
An automated visual PCB inspection is an approach used to counter difficulties occurred in human’s manual inspection that can eliminates subjective aspects and then provides fast, quantitative, and dimensional assessments. In this study, referential approach has been implemented on template and defective PCB images to detect numerous defects on bare PCBs before etching process, since etching usually contributes most destructive defects found on PCBs. The PCB inspection system is then improved by incorporating a geometrical image registration, minimum thresholding technique and median filtering in order to solve alignment and uneven illumination problem. Finally, defect classification operation is employed in order to identify the source for six types of defects namely, missing hole, pin hole, underetch, short-circuit, mousebite, and open-circuit.
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 | 2021-09-15 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 | 2008-05-28 18:41:53.0
This paper describes correlation between a true 2D area measurement (e.g. printer) and a height map generated area from a SPI system. In addition, this paper will explore the correlation between area/volume measurements and bridge detection between 2D/3D techniques. The ultimate goal is to arm the process engineers with information that can be used to make decision that will impact defects, cost, throughput and Return On Investment.
Technical Library | 2015-04-29 03:48:39.0
SPI equipment is routinely used in Printed Circuit Board (PCB) manufacturing to monitor and control one of the most crucial steps affecting the finished quality of circuit board. Solder paste deposition is the key process in board assembly operations using SMT techniques. Our LSM™ system was the industry's first popular method of manually inspecting solder paste; our SE systems revolutionized SMT production by offering an automated method for performing in-process 3D inspection on the assembly line. SPI systems measure the height and volume of the solder pads before the components are applied and the solder melted, and when used properly, can reduce the incidence of solder-related defects to statistically insignificant amounts. Critical to the SPI measurement is the accuracy of the height measurement because that has a direct correlation with solder volume and defects.
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 | 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 | 2015-04-29 03:29:56.0
Statistical Appearance Modelling technology enables an AOI system to “learn real world variation” based on operator interaction with inspection task results. This provides an accurate statistical description of normal variation in a product. With modelling technology, the user does not have to anticipate potential defects as the system will “flag” anything outside the “normal production range”. And, since the system is programmed with real production variation, it is sensitive to small subtle changes enabling reliable defect detection. Autonomous prediction of process variation enables an AOI system to be set up from a single PCB with production-ready performance. Setup time can be
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