Device-scale atomistic modelling of phase-change memory materials

YX Zhou and W Zhang and E Ma and VL Deringer, NATURE ELECTRONICS, 6, 746-+ (2023).

DOI: 10.1038/s41928-023-01030-x

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 x 20 x 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. A machine-learning-based model can be used to perform atomistic simulations of phase changes along the germanium-antimony-tellurium composition line, up to a full-size memory device model that contains half a million atoms.

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