Zschech P, Sager C, Siebers P, Pertermann M (2021)
Publication Language: German
Publication Type: Journal article
Publication year: 2021
Pages Range: 321–342
Open Access Link: https://link.springer.com/article/10.1365/s40702-020-00641-8
Quality assurance in the production of solar cells is a decisive factor for long-term performance guarantees on solar panels. This work contributes to this area in developing computer vision models to automatically detect defects on wafers by classifying electroluminescence images from a real manufacturing scenario. The challenge is not only to separate defective wafers from flawless ones but also to distinguish between specific types of defects while ensuring low inference times. For this purpose, simple statistical models, as well as different kinds of deep learning architectures based on convolutional neural networks (CNNs), are tested and compared with each other. Therefore, this work aims to evaluate multiple classification approaches of varying complexity levels while examining their practical applicability under real industrial conditions. The case study shows that all models have their right to exist and achieve excellent results in combination. While statistical models and simple CNNs provide reliable statements with accuracies up to 99% for defect types of simple to medium detectability, more advanced approaches based on region proposal networks are required once the defect images become more diffuse. The more advanced approaches allow a precise object localization of defects; however, they are also associated with increased labeling effort when annotating wafer images. Since the implementation of all models is based exclusively on open source tools such as TensorFlow, Keras, and OpenCV, the case study also demonstrates the possibilities offered by freely accessible solutions in the field of computer vision.
Zschech, P., Sager, C., Siebers, P., & Pertermann, M. (2021). Mit Computer Vision zur automatisierten Qualitätssicherung in der industriellen Fertigung: Eine Fallstudie zur Klassifizierung von Fehlern in Solarzellen mittels Elektrolumineszenz-Bildern. HMD : Praxis der Wirtschaftsinformatik, 321–342. https://dx.doi.org/10.1365/s40702-020-00641-8
Zschech, Patrick, et al. "Mit Computer Vision zur automatisierten Qualitätssicherung in der industriellen Fertigung: Eine Fallstudie zur Klassifizierung von Fehlern in Solarzellen mittels Elektrolumineszenz-Bildern." HMD : Praxis der Wirtschaftsinformatik (2021): 321–342.