Defect detection on optoelectronical devices to assist decision making: A real industry 4.0 case study Article Swipe
George P. Moustris
,
George Kouzas
,
Spyros Fourakis
,
Georgios Fiotakis
,
Apostolos Chondronasios
,
Abd Al Rahman M. Abu Ebayyeh
,
Alireza Mousavi
,
Kostas Apostolou
,
Jovana Milenkovic
,
Zoi Chatzichristodoulou
,
Erik Beckert
,
Jérémy Butet
,
Stéphane Blaser
,
Olivier Landry
,
Antoine Müller
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fmtec.2022.946452
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fmtec.2022.946452
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmtec.2022.946452
- https://www.frontiersin.org/articles/10.3389/fmtec.2022.946452/pdf
- OA Status
- diamond
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4292623479
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4292623479Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmtec.2022.946452Digital Object Identifier
- Title
-
Defect detection on optoelectronical devices to assist decision making: A real industry 4.0 case studyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-22Full publication date if available
- Authors
-
George P. Moustris, George Kouzas, Spyros Fourakis, Georgios Fiotakis, Apostolos Chondronasios, Abd Al Rahman M. Abu Ebayyeh, Alireza Mousavi, Kostas Apostolou, Jovana Milenkovic, Zoi Chatzichristodoulou, Erik Beckert, Jérémy Butet, Stéphane Blaser, Olivier Landry, Antoine MüllerList of authors in order
- Landing page
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https://doi.org/10.3389/fmtec.2022.946452Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fmtec.2022.946452/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fmtec.2022.946452/pdfDirect OA link when available
- Concepts
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Computer science, Middleware (distributed applications), Process (computing), Categorization, Wafer, Identification (biology), Artificial intelligence, Real-time computing, Engineering, Distributed computing, Operating system, Electrical engineering, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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