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http://bura.brunel.ac.uk/handle/2438/25073
Title: | Waveguide quality inspection in quantum cascade lasers: A capsule neural network approach |
Authors: | Abu Ebayyeh, AARM Mousavi, A Danishvar, S Blaser, S Gresch, T Landry, O Müller, A |
Keywords: | automatic optical inspection;capsule networks;convolutional neural networks;deep learning;defect inspection;optoelectronic industry;quantum cascade lasers |
Issue Date: | 11-Aug-2022 |
Publisher: | Elsevier |
Citation: | Abu Ebayyeh, A.A.R.M. et al. (2022) 'Waveguide quality inspection in quantum cascade lasers: A capsule neural network approach', Expert Systems with Applications, 210, 118421, pp. 1 - 12. doi: 10.1016/j.eswa.2022.118421. |
Abstract: | 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%. |
Description: | Data availability: The data that has been used is confidential. |
URI: | https://bura.brunel.ac.uk/handle/2438/25073 |
DOI: | https://doi.org/10.1016/j.eswa.2022.118421 |
ISSN: | 0957-4174 |
Other Identifiers: | ORCID iD: Abd Al Rahman M. Abu Ebayyeh https://orcid.org/0000-0001-5599-8005 ORCID iD: Alireza Mousavi https://orcid.org/0000-0003-0360-2712 ORCID iD: Sebelan Danishvar https://orcid.org/0000-0002-8258-0437 ORCID iD: Stéphane Blaser https://orcid.org/0000-0001-7579-0148 ORCID iD: Olivier Landry https://orcid.org/0000-0003-3850-7571 ORCID iD: Antoine Müller https://orcid.org/0000-0003-0521-5302 118421 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | 2.81 MB | Adobe PDF | View/Open |
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