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Device-scale atomistic modelling of phase-change memory materials

Abstract:
Computer simulations can play a central role in the understanding of phase-change materials and the development of advanced memory technologies. However, direct quantum-mechanical simulations are limited to simplified models containing a few hundred or thousand atoms. Here we report a machine-learning-based potential model that is trained using quantum-mechanical data and can be used to simulate a range of germanium–antimony–tellurium compositions—typical phase-change materials—under realistic device conditions. The speed of our model enables atomistic simulations of multiple thermal cycles and delicate operations for neuro-inspired computing, specifically cumulative SET and iterative RESET. A device-scale (40 × 20 × 20 nm3) model containing over half a million atoms shows that our machine-learning approach can directly describe technologically relevant processes in memory devices based on phase-change materials.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41928-023-01030-x

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Sub department:
Inorganic Chemistry
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Sub department:
Inorganic Chemistry
Oxford college:
St Anne's College
Role:
Author
ORCID:
0000-0001-6873-0278


Publisher:
Springer Nature
Journal:
Nature Electronics More from this journal
Volume:
6
Issue:
10
Pages:
746-754
Publication date:
2023-09-25
Acceptance date:
2023-08-11
DOI:
EISSN:
2520-1131


Language:
English
Pubs id:
1514226
Local pid:
pubs:1514226
Deposit date:
2023-08-22

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