Contribution to a conference proceedings/Contribution to a book

FZJ-2025-02591

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Unsupervised Machine Learning-based STEM diffraction pattern denoising for enhanced grain visualization in phase change materials

Ruzaeva, K.FZJ* ; Weber, D.FZJ* ; Werner, J. ; Sandfeld, S. (Corresponding author)FZJ*

2024
EDP Sciences Les Ulis

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Please use a persistent id in citations: doi:10.1051/bioconf/202412910022  doi:10.34734/FZJ-2025-02591

Abstract: Phase change materials (PCM) are an emerging class of materials in whichdifferent phases of the same material may have different optical, electric, ormagnetic properties and can be used as a phase change memory [1]. Phase-change memory materials, exemplified by (Ag, In)-doped Sb2Te (AIST) in thisresearch, have several advantages, including high-speed read and writeoperations, non-volatility, and a long lifespan [2]. PCMs are able to switchbetween amorphous and crystalline phases when subjected to heat orelectrical current. However, the full understanding of PCMs depends heavilyon accurate characterization, often through techniques such as scanningtransmission electron microscopy (STEM).

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Contributing Institute(s):

1. Materials Data Science and Informatics (IAS-9)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

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