Unsupervised Machine Learning-based STEM diffraction pattern denoising for enhanced grain visualization in phase change materials

BIO Web of Conferences, vol. 129, pp. 10022

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).

Authors 4

1. Karina Ruzaeva

   Forschungszentrum Jülich

   Affiliation as printed

   IAS-9, Forschungszentrum Jülich, Jülich, Germany

2. Dieter Weber

   Forschungszentrum Jülich · Ernst Ruska Centre

   Affiliation as printed

   ERC-1, Forschungszentrum Jülich, Jülich, Germany

3. Jonas Werner Aachen

   RWTH Aachen University

   Affiliation as printed

   GFE, RWTH Aachen University, Aachen, Germany

4. Stefan Sandfeld

   Forschungszentrum Jülich

   Affiliation as printed

   IAS-9, Forschungszentrum Jülich, Jülich, Germany