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

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.

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Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Martin Hytch reports financial support was provided by EUROPEAN UNION - IMPRESS. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.