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DC Field | Value | Language |
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dc.contributor.author | Abu Ebayyeh, AARM | - |
dc.contributor.author | Mousavi, A | - |
dc.contributor.author | Danishvar, S | - |
dc.contributor.author | Blaser, S | - |
dc.contributor.author | Gresch, T | - |
dc.contributor.author | Landry, O | - |
dc.contributor.author | Müller, A | - |
dc.date.accessioned | 2022-08-12T13:55:27Z | - |
dc.date.available | 2022-08-12T13:55:27Z | - |
dc.date.issued | 2022-08-11 | - |
dc.identifier | ORCID iD: Abd Al Rahman M. Abu Ebayyeh https://orcid.org/0000-0001-5599-8005 | - |
dc.identifier | ORCID iD: Alireza Mousavi https://orcid.org/0000-0003-0360-2712 | - |
dc.identifier | ORCID iD: Sebelan Danishvar https://orcid.org/0000-0002-8258-0437 | - |
dc.identifier | ORCID iD: Stéphane Blaser https://orcid.org/0000-0001-7579-0148 | - |
dc.identifier | ORCID iD: Olivier Landry https://orcid.org/0000-0003-3850-7571 | - |
dc.identifier | ORCID iD: Antoine Müller https://orcid.org/0000-0003-0521-5302 | - |
dc.identifier | 118421 | - |
dc.identifier.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. | en_US |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25073 | - |
dc.description | Data availability: The data that has been used is confidential. | en_US |
dc.description.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%. | en_US |
dc.description.sponsorship | European Union’s Horizon 2020 research and innovation programme under grant agreement No. 820677, iQonic project. | en_US |
dc.format.extent | 1 - 12 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | 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/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | automatic optical inspection | en_US |
dc.subject | capsule networks | en_US |
dc.subject | convolutional neural networks | en_US |
dc.subject | deep learning | en_US |
dc.subject | defect inspection | en_US |
dc.subject | optoelectronic industry | en_US |
dc.subject | quantum cascade lasers | en_US |
dc.title | Waveguide quality inspection in quantum cascade lasers: A capsule neural network approach | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.eswa.2022.118421 | - |
dc.relation.isPartOf | Expert Systems with Applications | - |
pubs.publication-status | Published | - |
pubs.volume | 210 | - |
dc.identifier.eissn | 1873-6793 | - |
dc.rights.holder | The Author(s) | - |
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 |
This item is licensed under a Creative Commons License