Simulation of the crystallization process of Ge₂Sb₂Te₅ nanoconfined in superlattice geometries for phase change memories

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{
  "id": "2689", 
  "created": "2025-05-16T11:49:37.597470+00:00", 
  "updated": "2025-05-23T08:24:31.714729+00:00", 
  "revision": 8, 
  "metadata": {
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    "license_addendum": null, 
    "edited_by": 576, 
    "publication_date": "May 23, 2025, 10:24:31", 
    "contributors": [
      {
        "affiliations": [
          "Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy"
        ], 
        "givennames": "Debdipto", 
        "familyname": "Acharya"
      }, 
      {
        "affiliations": [
          "Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy"
        ], 
        "givennames": "Omar", 
        "familyname": "Abou El Kheir"
      }, 
      {
        "affiliations": [
          "Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy"
        ], 
        "givennames": "Simone", 
        "familyname": "Marcorini"
      }, 
      {
        "email": "marco.bernasconi@unimib.it", 
        "affiliations": [
          "Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy"
        ], 
        "givennames": "Marco", 
        "familyname": "Bernasconi"
      }
    ], 
    "description": "Phase change materials are the most promising candidates for the realization of artificial synapsis for neuromorphic computing. Different resistance levels corresponding to analogic values of the\nsynapsis conductance can be achieved by modulating the size of an amorphous region embedded in its crystalline matrix. Recently, it has been proposed that a superlattice made of alternating layers of the phase change compound Sb\u2082Te\u2083 and of the TiTe\u2082 confining material 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. In this work, we consider to substitute Sb\u2082Te\u2083 with the Ge\u2082Sb\u2082Te\u2085 prototypical phase change compound that should feature better data retention. By exploiting molecular dynamics simulations with a machine learning interatomic potential, we have investigated the crystallization kinetics of Ge\u2082Sb\u2082Te\u2085 nanoconfined in geometries mimicking Ge\u2082Sb\u2082Te\u2085/TiTe\u2082 superlattices. It turns out that nanoconfinement induces a slight reduction in the crystal growth velocities with respect to the bulk, but also an enhancement of the nucleation rate due to heterogeneous nucleation. The results support the idea of investigating Ge\u2082Sb\u2082Te\u2085/TiTe\u2082 superlattices for applications in neuromorphic devices with improved data retention. The effect on the crystallization kinetics of the addition of van der Waals interaction to the interatomic potential is also discussed.", 
    "id": "2689", 
    "owner": 1537, 
    "references": [
      {
        "doi": "10.1039/d5nr00283d", 
        "type": "Journal reference", 
        "citation": "D. Acharya, O. Abou El Kheir, S. Marcorini, M. Bernasconi, Nanoscale, (2025)"
      }
    ], 
    "version": 1, 
    "license": "Creative Commons Attribution 4.0 International", 
    "status": "published", 
    "title": "Simulation of the crystallization process of Ge\u2082Sb\u2082Te\u2085 nanoconfined in superlattice geometries for phase change memories", 
    "keywords": [
      "molecular dynamics simulation", 
      "Neural Network Potential", 
      "Crystallization", 
      "Phase Change Materials", 
      "Neuromophic Computing"
    ], 
    "doi": "10.24435/materialscloud:t4-kf", 
    "mcid": "2025.80", 
    "is_last": true, 
    "_oai": {
      "id": "oai:materialscloud.org:2689"
    }, 
    "conceptrecid": "2688"
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}