Technical Library | 2015-06-30 22:02:41.0
This paper describes the losses from defects at the placement process in the SMT line. Two case studies of European and Taiwanese SMT manufacturers illustrate the actual losses from their defects. An evaluation method to select a pre-reflow AOI system maximizing the return on investment (ROI) is introduced. In the end, ROIs of three commercial pre-reflow AOI systems are compared to demonstrate the importance of selecting an appropriate AOI system. This paper will increase the probability that anyone installing an AOI system during the pre-reflow process will obtain a successful gain with short payback period.
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 | 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 | 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
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.
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