Berlin 2024 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 64: Liquid and Amorphous Materials IV
MM 64.3: Vortrag
Donnerstag, 21. März 2024, 17:15–17:30, C 243
Device-scale atomistic modelling of phase-change memory materials using a machine-learned interatomic potential — •Yuxing Zhou1,2, Wei Zhang2, En Ma2, and Volker L. Deringer1 — 1Department of Chemistry, University of Oxford, UK — 2Center for Alloy Innovation and Design, Xi'an Jiaotong University, China
Phase-change materials (PCM) are leading candidates for next-generation memory and neuromorphic computing chips. The Ge--Sb--Te alloys on the GeTe-Sb2Te3 tie-line (referred to as ``GST'') have been most widely studied and used in commercial memory products. Quantum-accurate computer simulations have played a central role in understanding complex GST alloys. However, the large computational cost has precluded simulations on the length scales of real devices. In this presentation, we describe a single, compositionally flexible machine-learning interatomic potential with a quantum-mechanical level of accuracyy. We show that our model can describe the flagship GST alloys under various practical device conditions, e.g., non-isothermal heating, and taking chemical disorder into account. The superior computing efficiency of the new approach enables the simulation of multiple thermal cycles. We also show a device-scale capability demonstration in a real device model of more than 500,000 atoms. These describe technologically relevant processes in realistic memory products. Our work demonstrates how atomistic ML-driven simulations can help study the structural and chemical properties as well as programming mechanisms of GST devices.
Keywords: phase-change memory materials; machine-learning potential; device-scale atomistic simulations