<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Acharya, Debdipto</dc:creator> <dc:creator>Abou El Kheir, Omar</dc:creator> <dc:creator>Campi, Davide</dc:creator> <dc:creator>Bernasconi, Marco</dc:creator> <dc:date>2024-02-09</dc:date> <dc:description>Superlattices made of alternating blocks of the phase change compound Sb₂Te₃ and of TiTe₂ confining layers have been recently proposed for applications in neuromorphic devices. The Sb₂Te₃/TiTe₂ heterostructure allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. However, Sb₂Te₃ suffers from a low data retention due to a low crystallization temperature Tx. Substituting Sb₂Te₃ with a phase change compound with a higher Tx, such as GeTe, seems an interesting option in this respect. Nanoconfinement might, however, alters the crystallization kinetics with respect to the bulk. In this work, we investigated the crystallization process of GeTe nanoconfined in geometries mimicking GeTe/TiTe₂ superlattices by means of molecular dynamics simulations with a machine learning potential. The simulations reveal that nanoconfinement induces a mild reduction in the crystal growth velocities which would not hinder the application of GeTe/TiTe₂ heterostructures in neuromorphic devices with superior data retention.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2024.25</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:5k-vh</dc:identifier> <dc:identifier>mcid:2024.25</dc:identifier> <dc:identifier>oai:materialscloud.org:2074</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>Neural Network Potential</dc:subject> <dc:subject>Crystallization</dc:subject> <dc:subject>Phase Change Memories</dc:subject> <dc:subject>Phase Change Materials</dc:subject> <dc:subject>Molecular Dynamics Simulation</dc:subject> <dc:subject>GeTe</dc:subject> <dc:subject>Nanoconfinement</dc:subject> <dc:subject>Superlattices</dc:subject> <dc:subject>Neuromophic Computing</dc:subject> <dc:title>Crystallization kinetics of nanoconfined GeTe slabs in GeTe/TiTe-like superlattices for phase change memories</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>