Principle of TEM alignment using convolutional neural networks: Case study on condenser aperture alignment

Loïc Grossetête (1, 2) , Cécile Marcelot (3) , Christophe Gatel (2) , Sylvain Pauchet (1) , Martin Hytch (2)

1 LII - ENAC - Equipe Informatique Interactive
2 CEMES-I3EM - Interférométrie, In situ et Instrumentation pour la Microscopie Electronique
3 CEMES - Centre d'élaboration de matériaux et d'études structurales

The possibility of automatically aligning the transmission electron microscope (TEM) is explored using an approach based on artificial intelligence (AI). After presenting the general concept, we test the method on the first step of the alignment process which involves centering the condenser aperture. We propose using a convolutional neural network (CNN) that learns to predict the x and y-shifts needed to realign the aperture in one step. The learning data sets were acquired automatically on the microscope by using a simplified digital twin. Different models were tested and analysed to choose the optimal design. We have developed a human-level estimator and intend to use it safely on all apertures. A similar process could be used for most steps of the alignment process with minimal changes, allowing microscopists to reduce the time and training required to perform this task. The method is also compatible with continuous correction of alignment drift during lengthy experiments or to ensure uniformity of illumination conditions during data acquisition.

Mots clés: Artificial intelligence; Convolutional neural networks; Automatic alignment

Domaines: Physique [physics]; Informatique [cs]

Loïc Grossetête, Cécile Marcelot, Christophe Gatel, Sylvain Pauchet, Martin Hytch. Principle of TEM alignment using convolutional neural networks: Case study on condenser aperture alignment. Ultramicroscopy, 2024, 267, pp.114047. ⟨10.1016/j.ultramic.2024.114047⟩. ⟨hal-04771442⟩