Abstract
Superlattices made of alternating blocks of the phase change compound Sb[Formula: see text]Te[Formula: see text] and of TiTe[Formula: see text] confining layers have been recently proposed for applications in neuromorphic devices. The Sb[Formula: see text]Te[Formula: see text]/TiTe[Formula: see text] 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, Sb[Formula: see text]Te[Formula: see text] suffers from a low data retention due to a low crystallization temperature T[Formula: see text]. Substituting Sb[Formula: see text]Te[Formula: see text] with a phase change compound with a higher T[Formula: see text], 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/TiTe[Formula: see text] 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/TiTe[Formula: see text] heterostructures in neuromorphic devices with superior data retention.