Abstract
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.
Original language | English |
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Publication status | Published - 21 Apr 2022 |
Event | 12th ASEM Workshop: Austrian Society for Electron Microscopy - JKU Linz, Linz, Austria Duration: 21 Apr 2022 → 22 Apr 2022 https://asem.at/events/12th-asem-workshop/ |
Conference
Conference | 12th ASEM Workshop |
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Country/Territory | Austria |
City | Linz |
Period | 21/04/22 → 22/04/22 |
Internet address |
ASJC Scopus subject areas
- Materials Science(all)
Fields of Expertise
- Advanced Materials Science
Treatment code (Nähere Zuordnung)
- Basic - Fundamental (Grundlagenforschung)