Software defect process flow4/9/2023 ![]() ![]() Zero-defect-manufacturing by definition is a “holistic approach for ensuring both process and product quality by reducing defects through corrective, preventive, and predictive techniques, using mainly data-driven technologies and guaranteeing that no defective products leave the production site and reach the customer, aiming at higher manufacturing sustainability.” ( Psarommatis et al., 2022) Our proposed solution considers the optimisation and design of the entire process chain and the assembly process for optoelectronic components and devices. This paper aims to present a flexible and scalable zero-defect manufacturing solution for systems with optoelectronics components. Consequently, full and generic zero-defect production infrastructures in the manufacturing of optoelectronics and photonics rarely can be found, although there are highly individualized, singular functionalities implementations available that often also rely on specialized know-how of the human workforce. The combination of both bulk volume and surface structure properties of optoelectronical components usually also don’t enable for rework nor recycling. System integration, on the other hand, is often characterized by high demands on cleanliness and accuracy, already dealing with high-value components that render single failures in manufacturing a high economical risk. A long and complex value chain starts with individual components manufacturing by classical optical (grinding, polishing) as well as lithographic wafer processing technologies, whereas also bulk material properties significantly contribute to the component’s performance. Optoelectronic and photonic components and systems pose specific challenges for zero-defect manufacturing. Furthermore, when reducing the defects and increasing the yield, the assembly costs in terms of nonmaterial expenses, scrap and rework costs are reduced respectively as well. Specifically, the improvement of process efficiency and yield is obtained by the deployment of automation, where the quality is increased by minimising the generation of defects. Thus, we are becoming witnesses to the introduction of new processes and technologies in optoelectrical manufacturing, towards digital, virtual, flexible and resource-efficient factories ( Mourtzis et al., 2022) ( Mourtzis and Doukas, 2014). Due to the increased customization requirements, not to mention the added complexity of planning and control of production systems, it appears that the manufacturing is only affordable when performed in many stages and in multiple locations. ![]() Nowadays, the demand for optoelectronic devices is rising while, on the other hand, the optoelectrical manufacturing is facing significant challenges in dealing with the evolution of the equipment, instrumentation and manufacturing processes they support. 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. The proposed solution has been implemented on a real industrial use-case in laser manufacturing. 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. To automate the entire process, a communication middleware called Higher Level Communication Middleware (HLCM) is used for orchestrating the information between the processing steps. One using low resolution grating images of the wafer, and the other using high resolution surface scan images acquired with a microscope. The system provides two image-based defect detection pipelines. 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. 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. 6Alpes Lasers SA, Neuchâtel, Switzerland.5Fraunhofer Institute for Applied Optics and Precision Engineering (IOF), Jena, Germany.4ATLANTIS Engineering S.A., Thessaloniki, Greece.3Brunel University, Uxbridge, LN, United Kingdom.Abu Ebayyeh 3, Alireza Mousavi 3, Kostas Apostolou 4, Jovana Milenkovic 4*, Zoi Chatzichristodoulou 4, Erik Beckert 5, Jeremy Butet 6, Stéphane Blaser 6, Olivier Landry 6 and Antoine Müller 6 Moustris 1, George Kouzas 1, Spyros Fourakis 2, Georgios Fiotakis 2, Apostolos Chondronasios 2, Abd Al Rahman M. ![]()
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