Technical Library | 2012-02-23 21:16:28.0
Installed for the first time 20 years ago, Automated Optical Inspection (AOI) more recently has become an essential part of our SMT environment. Today, most process engineers are turning to machines as an inspection strategy for addressing quality and pro
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 | 2009-08-26 19:32:32.0
Automated optical inspection (AOI) and automated X-ray inspection (AXI) have been around for some time in various configurations and both have played a role in improving the quality of circuit boards. While some companies opt for one technology over the other, each form of inspection contributes its own unique benefit to the manufacturing process.
Technical Library | 2017-08-28 17:14:41.0
PCB suppliers in the automotive space are vastly accelerating their time to market by using automated optical inspection (AOI) systems during PCB assembly. However, this next-generation technique is not limited in scope to the automotive industry – it has powerful implications for the entire PCB industry.
Technical Library | 2021-05-06 13:41:55.0
Quality control is of vital importance during electronics production. As the methods of producing electronic circuits improve, there is an increasing chance of solder defects during assembling the printed circuit board (PCB). Many technologies have been incorporated for inspecting failed soldering, such as X-ray imaging, optical imaging, and thermal imaging. With some advanced algorithms, the new technologies are expected to control the production quality based on the digital images. However, current algorithms sometimes are not accurate enough to meet the quality control. Specialists are needed to do a follow-up checking. For automated X-ray inspection, joint of interest on the X-ray image is located by region of interest (ROI) and inspected by some algorithms. Some incorrect ROIs deteriorate the inspection algorithm.
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 | 2022-02-21 19:49:16.0
The ability to undertake non-destructive testing on semiconductor devices, during both their manufacture and their subsequent use in printed circuit boards (PCBs), has become ever more important for checking product quality without compromising productivity. The use of x-ray inspection not only provides a potentially non-destructive test but also allows investigation within optically hidden areas, such as the wire bonding within packages and the quality of post solder reflow of area array devices (e.g. BGAs, CSPs and flip chips).
Technical Library | 2019-01-23 21:33:32.0
Automated Optical Inspection (AOI) is advantageous in that it enables defects to be detected early in the manufacturing process, reducing the Cost of Repair as the AOI systems identify the specific components that are failing removing the need for any additional test troubleshooting1-3. Because of this, more Electronic Contract Manufacturing Services (EMS) companies are implementing AOI into their SMT lines to minimize repair costs and maintain good process and product quality, especially for new component types. This project focuses on the testing of component package 03015 which is challenging for AOI.
Technical Library | 2021-11-22 20:32:10.0
The aim of this work is to define a procedure to develop diagnostic systems for Printed Circuit Boards, based on Automated Optical Inspection with low cost and easy adaptability to different features. A complete system to detect mounting defects in the circuits is presented in this paper. A low cost image acquisition system with high accuracy has been designed to fit this application. Afterward, the resulting images are processed using the Wavelet Transform and Neural Networks, for low computational cost and acceptable precision. The wavelet space represents a compact support for efficient feature extraction with the localization property. The proposed solution is demonstrated on several defects in different kind of circuits.
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).
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