Semi-automatic tools for nanoscale metrology and annotations for deep learning automation on electron microscopy images

Isaac Wilfried Sanou; Julien Baderot; Yannick Benezeth; Stéphanie Bricq; Franck Marzani; Sergio Martinez; Johann Foucher

doi:10.1117/12.2690493

28 July 2023 Semi-automatic tools for nanoscale metrology and annotations for deep learning automation on electron microscopy images

Isaac Wilfried Sanou, Julien Baderot, Yannick Benezeth, Stéphanie Bricq, Franck Marzani, Sergio Martinez, Johann Foucher

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Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 127490D (2023) https://doi.org/10.1117/12.2690493
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France

Abstract

For semiconductor applications, billions of objects are manufactured for a single device such as a central processing unit (CPU), storage drive, or graphical processing unit (GPU). To obtain functional devices, each element of the device has to follow precise dimensional and physical specifications at the nanoscale. Generally, the pipeline consists to annotate an object in an image and then take the measurements of the object. Manually annotating images is extremely time-consuming. In this paper, we propose a robust and fast semi-automatic method to annotate an object in a microscopy image. The approach is a deep learning contour-based method able first to detect the object and after finding the contour thanks to a constraint loss function. This constraint follows the physical meaning of electron microscopy images. It improves the quality of boundary detail of the vertices of each object by matching the predicted vertices and most likely the contour. The loss is computed during training for each object using a proximal way of our dataset. The approach was tested on 3 different types of datasets. The experiments showed that our approaches can achieve state-of-the-art performance on several microscopy images dataset.

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Isaac Wilfried Sanou, Julien Baderot, Yannick Benezeth, Stéphanie Bricq, Franck Marzani, Sergio Martinez, and Johann Foucher "Semi-automatic tools for nanoscale metrology and annotations for deep learning automation on electron microscopy images", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 127490D (28 July 2023); https://doi.org/10.1117/12.2690493

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KEYWORDS

Electron microscopy

Deep learning

Image segmentation

Education and training

Metrology

Scanning electron microscopy

Laser sintering

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