Machine-learning-driven atomistic simulations are shown to describe phase-change materials on the length scale of real devices. This demonstration suggests that the atomic-scale design of phase-change architectures, programming conditions and full devices could be within reach.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Zhang, W., Mazzarello, R., Wuttig, M. & Ma, E. Designing crystallization in phase-change materials for universal memory and neuro-inspired computing. Nat. Rev. Mater. 4, 150–168 (2021). A review article that presents current and future applications of PCMs and strategies for their computational design.
Hegedüs, J. & Elliott, S. R. Microscopic origin of the fast crystallization ability of Ge–Sb–Te phase-change memory materials. Nat. Mater. 7, 399–405 (2008). This paper reports one of the early applications of quantum-mechanical simulations to PCMs.
Deringer, V. L., Caro, M. A. & Csányi, G. Machine learning interatomic potentials as emerging tools for materials science. Adv. Mater. 31, 1902765 (2019). A progress report that focuses on the ways that ML potentials can be applied to materials.
Sosso, G. C. et al. Neural network interatomic potential for the phase change material GeTe. Phys. Rev. B 85, 174103 (2012). This paper reports a ML potential for binary GeTe that enabled many subsequent applications.
Cheng, H.-Y. et al. 3D cross-point phase-change memory for storage-class memory. J. Phys. D 52, 473002 (2019). A review article that discusses the use of PCMs in cross-point memory devices.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Zhou, Y. et al. Device-scale atomistic modelling of phase-change memory materials. Nat. Electron. https://doi.org/10.1038/s41928-023-01030-x (2023).
Rights and permissions
About this article
Cite this article
Atomic-scale machine learning for modelling memory devices. Nat Electron 6, 726–727 (2023). https://doi.org/10.1038/s41928-023-01031-w
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41928-023-01031-w
