Remote PACBED Thickness Determination by CNNs

Michael Oberaigner, Dieter Weber, Alexander Clausen, Daniel Knez, Gerald Kothleitner

Publikation: KonferenzbeitragPoster


A convenient thickness determination technique for crystalline samples is the position averaged convergent-beam electron diffraction(PACBED) method [1]. The thickness is determined by finding the best match of the recorded PACBED pattern with a series ofsimulated PACBEDs. This process can be automatized by convolutional neural networks (CNNs), making the method fast and easy toapply [2]. However, the simulation of a synthetic dataset and the training of the CNNs have high computational cost and these CNNsare only valid for the specific trained system. Therefore, we built a working prototype of a server-based thickness determination byCNNs with a shared CNN-database and a GUI. By this, every scientist, even without knowledge about machine learning andmultislice simulation, could determine the specimen thickness by PACBEDs within few seconds during a microscope session.
PublikationsstatusVeröffentlicht - 21 Apr. 2022
Veranstaltung12th ASEM Workshop: Austrian Society for Electron Microscopy - JKU Linz, Linz, Österreich
Dauer: 21 Apr. 202222 Apr. 2022


Konferenz12th ASEM Workshop

ASJC Scopus subject areas

  • Werkstoffwissenschaften (insg.)

Fields of Expertise

  • Advanced Materials Science

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)

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