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Communication Dans Un Congrès Année : 2023

Semi-automatic tools for nanoscale metrology and annotations for Deep Learning automation on Electron Microscopy images

Résumé

For semiconductor applications, billions of objects are manufactured for a single device such as central processing unit (CPU), storage drive or graphical processing unit (GPU). In order to obtain functional devices, each individual element of the device has to follow precise specifications at nanoscale. For that, it is essential to obtain efficiently a pipeline to automate measurements on a set of similar images of semiconductor objects captured with electron microscopy. Generally, the pipeline consists to annotate an object in a image and then take the measurements of the object. Manually images annotation is extremely time consuming. In this paper, we propose a semi-automatic method to annotate an object in a microscopy image based on deep learning with the precision of the measurement. The approach is a deep learning contour-based method able first to detect the object and after find the contour thanks to a constraint loss function. This constraint improves the quality of boundary detail of the vertices of each object by matching the predicted vertices and the most likely ground truth. The loss is computed during training for each object using a proximal way of our dataset. The experiments showed that our approaches can achieve a state-of-the-art performance on several microscopy images dataset.
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Dates et versions

hal-04296206 , version 1 (20-11-2023)

Identifiants

  • HAL Id : hal-04296206 , version 1

Citer

Isaac Wilfried Sanou, Julien Barderot, Yannick Benezeth, Stéphanie Bricq,, Franck Marzani, et al.. Semi-automatic tools for nanoscale metrology and annotations for Deep Learning automation on Electron Microscopy images. Quality Control by Artificial Vision, Jun 2023, Albi, France. ⟨hal-04296206⟩
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