BackgroundSimilar to many other industries, semiconductor manufacturing is undergoing a digital transformation. The wafer fabs have been highly automated for a few years, and data are everywhere, in high volume, heterogeneous, and not always structured. Data analytics for manufacturing is becoming a key competence to be embedded in the daily lives of wafer fabs engineers. Among the wide variety of data, this paper focuses on images and their classification by convolutional neural networks (CNN), which are illustrated by various use cases in the manufacturing environment. AimPractically, in over a year in a wafer fab, millions of pictures are generated. Most of the time these images are treated directly by metrology and inspection tools, and humans eventually look at these only if a measurement alarm (measurement quality or out-of-specification value) is reported. Is there any interest in reviewing all of them? Technically, images are underused data sources of information because they contain a lot of relevant information that is not captured. The return on investment of inspecting all images is highly questionable if the review needs to be done by humans. When done by the operator, this task is time-consuming and prone to human interpretation, which can lead to variability in the result. The aim of this work is to show that CNNs are perfectly adapted to wafer fabs and can take on this workload in a very efficient way. ApproachSince the middle of the last decade, new tools (software and hardware) have emerged to solve these massive classification needs, including (1) deep learning algorithms and particularly CNNs or region-based CNNs (RCNN), (2) graphical processor unit (GPU) to speed up training time, (3) distributed database to store high volumes of data, and (4) open-source community driven by python eco-systems to support proof of concept build-ups and customized solutions. ResultsThis paper will emphasize the integration of these deep learning solutions into the daily life of semiconductor manufacturing with a focus on transfer learning, which provides high versatility of CNNs/R-CNNs solutions with respect to different use cases. ConclusionsA unique CNN infrastructure has been set up to offer wafer fab engineers a versatile solution adapted to many different kinds of images and use cases to demonstrate that CNN image classification can be smoothly embedded in daily tasks.
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