Technical Library | 2013-08-07 21:52:15.0
PCB architectures have continued their steep trend toward greater complexities and higher component densities. For quality control managers and test technicians, the consequence is significant. Their ability to electrically test these products is compounded with each new generation. Probe access to high density boards loaded with micro BGAs using a conventional in-circuit (bed-of-nails) test system is greatly reduced. The challenges and complexity of creating a comprehensive functional test program have all but assured that functional test will not fill the widening gap. This explains why sales of automated-optical and automated X-ray inspection (AOI and AXI) equipment have dramatically risen...
Technical Library | 2023-11-20 18:49:11.0
Non-destructive testing during the manufacture of printed wiring boards (PWBs) has become ever more important for checking product quality without compromising productivity. Using x-ray inspection, not only provides a non-destructive test but also allows investigation within optically hidden areas, such as the quality of post solder reflow of area array devices (e.g. BGAs, CSPs and flip chips). As the size of components continues to diminish, today's x-ray inspection systems must provide increased magnification, as well as better quality x-ray images to provide the necessary analytical information. This has led to a number of x-ray manufacturers offering digital x-ray inspection systems, either as standard or as an option, to satisfy these needs. This paper will review the capabilities that these digital x-ray systems offer compared to their analogue counterparts. There is also a discussion of the various types of digital x-ray systems that are available and how the use of different digital detectors influences the operational capabilities that such systems provide.
Technical Library | 2013-07-02 16:44:31.0
AOI and AXI systems can address multiple tasks in various locations of the manufacturing process and have become the leading technologies in the quest to identify defects and improve process yields.
Technical Library | 2010-09-16 18:45:06.0
With PCB complexity and density increasing and also wider use of 3D devices, tougher requirements are now imposed on device inspection both during original manufacture and at their subsequent processing onto printed circuit boards. More complicated and de
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 | 2023-11-20 17:42:33.0
Zero-defect strategies and increased demands on the production of assemblies are making quality assurance in electronics production increasingly important. Continous miniaturization of components, ever higher packing densities and the associated hard-to-view assembly areas, as well as the increased use of components such as BGAs, QFNs and QFPs, pose a considerable challenge when it comes to high-precision quality control.
Technical Library | 2012-11-29 14:23:58.0
1000 units per day) production environment presents challenging technical, logistic and cost obstacles that are usually more complex than those encountered at the inspection (automated optical inspection) and the manufacturing process test step (in-circuit test).
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 | 2021-11-22 20:39:44.0
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided greater data availability. Such data availability has spurred the development of artificial intelligence models, which allow higher degrees of automation and reduced bias when inspecting the products. Furthermore, the increased speed of inspection reduces overall costs and time required for defect inspection. In this research, we compare five streaming machine learning algorithms applied to visual defect inspection with real world data provided by Philips Consumer Lifestyle BV. Furthermore, we compare them in a streaming active learning context, which reduces the data labeling effort in a real-world context. Our results show that active learning reduces the data labeling effort by almost 15% on average for the worst case, while keeping an acceptable classification performance. The use of machine learning models for automated visual inspection are expected to speed up the quality inspection up to 40%.
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