A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

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.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Assessing the Potential for Civil-Military Integration: Selected Case Studies

This paper presents material from three of the case studies undertaken during the assessment: flat panel displays, polymeric composites, and shipbuilding. The three cases illustrate both the opportunities and the challenges facing those designing policies to increase the level of civil–military integration (CMI)

JTEC Panel report on electronic manufacturing and packaging in Japan

This report summarizes the status of electronic manufacturing and packaging technology in Japan in comparison to that in the United States, and its impact on competition in electronic manufacturing in general. In addition to electronic manufacturing technologies, the report covers technology and manufacturing infrastructure, electronics manufacturing and assembly, quality assurance and reliability in the Japanese electronics industry, and successful product realization strategies. The panel found that Japan leads the United States in almost every electronics packaging technology. Japan clearly has achieved a strategic advantage in electronics production and process technologies. Panel members believe that Japanese competitors could be leading U.S. firms by as much as a decade in some electronics process technologies

Automatic Inspection Method for Macro Defects in TFT-LCD Color Filter Fabrication Process

Defect inspection of thin film transistor liquid crystal displays (TFT-LCDs) is divided into two steps: detection and judgment. This letter proposes an automatic detection and judgment method for macro defects in the TFT-LCD color filter (CF) fabrication process using the diffraction pattern shift and chromaticity, respectively. The proposed method is verified via experiments using sampled CF glasses with macro defects, which were judged as PASS (no defect) in the CF fabrication process by a human operator who inspects CF glasses using conventional inspection systems, but were rejected in the module process. Seventeen rejected glasses were used in the experiments. All macro defects, including non-uniformity under 300Å, were detected and are judged as REJECT (defect) using the proposed method

NASA Tech Briefs, May 1996

Topics include: Video and Imaging;Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Report

Technology 2004, Vol. 2

Proceedings from symposia of the Technology 2004 Conference, November 8-10, 1994, Washington, DC. Volume 2 features papers on computers and software, virtual reality simulation, environmental technology, video and imaging, medical technology and life sciences, robotics and artificial intelligence, and electronics

NASA Tech Briefs, October 1992

Topics covered include: Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication technology; Mathematics and Information Sciences; Life Sciences

Monitoring of a carbon anode paste manufacturing process using machine vision and latent variable methods

Le procédé de réduction électrolytique Hall-Héroult est utilisé pour la fabrication industrielle d’aluminium primaire. Ce procédé nécessite l’utilisation d'anodes de carbone. L’uniformité de la qualité de celles-ci est un paramètre très important pour assurer la stabilité et des performances optimales des cuves d’électrolyse. Malheureusement, les fabricants d'anodes sont actuellement confrontés à une augmentation de la variabilité des matières premières. Cette situation est due à une diminution de la disponibilité de matières premières de bonne qualité à faibles coûts. Pour compenser, les fabricants d'anodes doivent diversifier leur choix de fournisseurs, ce qui augmente la variabilité. Cependant, les usines ne sont pas préparées pour réagir à cette situation tout en maintenant une qualité d'anode stable. Cette situation est due, entre autres, à un manque de mesures quantitatives en temps réel de la qualité des anodes. Plusieurs exemples d’applications industrielles de vision numérique ont été présentés dans la littérature. Par conséquent, il existe une opportunité de développer un tel système pour obtenir une mesure non destructive et en temps réel de la qualité de la pâte d'anode. Le développement du capteur a été fait avec de la pâte et des anodes pressées à l'échelle laboratoire. Un ensemble de caractéristiques de texture d'images calculées à partir de la transformée en ondelettes discrète (DWT) et de matrices de cooccurrence de niveaux de gris (GLCM) ont été sélectionnées. Ces caractéristiques étaient sensibles aux variations dans la formulation et de la quantité de brai dans la pâte. Le capteur est aussi capable de détecter la quantité optimale de brai (OPD) pour différents cokes. Ensuite, la sensibilité et la robustesse du capteur ont été testées avec de la pâte industrielle. Finalement, les usines collectent déjà beaucoup de mesures de procédé en temps réel. Ces données peuvent être utilisées dans une stratégie de monitorage statistique pour détecter et investiguer des déviations de qualité. Une nouvelle méthode statistique multivariée par variables latentes PLS multi-blocs séquentiels (SMB-PLS) a été développée pour améliorer l'interprétation des données industrielles par rapport aux méthodes usuelles de PLS multi-blocs. Cette méthode a également été utilisée pour discuter de la pertinence d’utiliser les caractéristique d'image de la pâte à un modèle statistique pour la surveillance de la variabilité du procédé.The Hall-Héroult electrolysis reduction process used for the industrial aluminium smelting relies on the consumption of carbon anodes. The quality and consistency of these anodes are very important for the stability and performance of the reduction cells. Unfortunately, the anode manufacturers currently face an increase in the raw material variability. This is due to the declining availability of high quality, low cost and consistent materials on the market forcing the anode manufacturers to diversify their suppliers. However, the anode plants are not prepared to compensate for this increase in variability and still maintain consistent anode quality. There is a lack of real-time quality monitoring and control of the baked anodes properties and the most important raw material and process parameters. Machine vision applications have been successful in many industrial applications. Therefore there is an opportunity to develop such a system to obtain a non destructive and online measurement of the anode paste quality. This sensor could then be used in a feedback/feedforward control strategy for attenuating the unmeasured raw material and process variations. The sensor development was performed using laboratory scale paste and pressed anodes. A set of image texture features computed from discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods were selected. These features could capture variations in formulation, pitch ratio in the paste and in pitch demand. The sensor was also found to be sensitive to the optimum pitch demand (OPD) of two different cokes. Then, the sensitivity and robustness of the sensor was tested using industrial paste. Finally, the anode plants already collect some real-time process measurement and off-line raw material and baked anode properties that can be used to monitor and troubleshoot process and quality deviations. A new sequential multi-block PLS (SMB-PLS) method was developed to improve the interpretation of complex industrial dataset compared to already available multi-block PLS methods. This method was also used to discuss the relevance of adding real-time paste image feature to a statistical model for monitoring of the process variability