4 research outputs found
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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
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Printed Circuit Board Inspection: Fusion of Optical and X-ray Images (FOXi) for Electronic Components Classification
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Waveguide quality inspection in quantum cascade lasers: A capsule neural network approach
Data availability: The data that has been used is confidential.Copyright © 2022 The Author(s). Growing demand for consumer electronic devices and telecommunications is expected to drive the quantum cascade laser (QCL) market. The increase in the production rate of QCLs increases the likelihood of production failures and anomalies. The detection of waveguide defects and dirt using automatic optical inspection (AOI) and deep learning (DL) is the main focus of this study. The images samples of QCLs were collected from a laser manufacturing plant in Europe. Due to the lack of sufficient dirt and defect samples, automatic and manual data augmentation approaches were used to increase the number of images. A combination of an improved capsule neural network (WaferCaps) and convolutional neural network (CNN) based on parallel decision fusion is used to classify the samples. The output of these classifiers were combined based on rule-based selection algorithm that chooses the performance of the best classifier according to the class. The proposed approach was compared with the performance of standalone models, different state-of-the-art DL models such as CapsNet, ResNet-50, MobileNet, DenseNet, Xception and Inception-V3 and other machine learning (ML) models such as Support Vector Machine (SVM), decision tree, -NN and Multi-layer Perceptron (MLP). The proposed approach outperformed them all with a validation accuracy of 98.5%.European Union’s Horizon 2020 research and innovation programme under grant agreement No. 820677, iQonic project
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Defect detection on optoelectronical devices to assist decision making: A real industry 4.0 case study
This paper presents an innovative approach, based on industry 4.0 concepts, for
monitoring the life cycle of optoelectronical devices, by adopting image
processing and deep learning techniques regarding defect detection. The
proposed system comprises defect detection and categorization during the
front-end part of the optoelectronic device production process, providing a
two-stage approach; the first is the actual defect identification on individual
components at the wafer level, while the second is the pre-classification of
these components based on the recognized defects. The system provides two
image-based defect detection pipelines. One using low resolution grating
images of the wafer, and the other using high resolution surface scan
images acquired with a microscope. To automate the entire process, a
communication middleware called Higher Level Communication Middleware
(HLCM) is used for orchestrating the information between the processing steps.
At the last step of the process, a Decision Support System (DSS) collects all
information, processes it and labels it with additional defect type categories, in
order to provide recommendations to the optoelectronical engineer. The
proposed solution has been implemented on a real industrial use-case in
laser manufacturing. Analysis shows that chips validated through the
proposed process have a probability to lase at a specific frequency six times
higher than the fully rejected ones.European Union’s
Horizon 2020—the Framework Programme for Research and
Innovation (2014–2020) under grant agreement No
820677—Innovative strategies, sensing and process Chains for
increased Quality, re-configurability, and recyclability of
Manufacturing Optolectronics (iQonic