R&D: Unlocking Device-Scale Atomistic Modelling of Phase-Change Memory Materials

arxiv.org has published an article written by Yuxing Zhou, Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, United Kingdom, and Center for Alloy Innovation and Design (CAID), State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, Xi’an 710049, China, Wei Zhang, En Ma, Center for Alloy Innovation and Design (CAID), State Key Laboratory for Mechanical Be-havior of Materials, Xi’an Jiaotong University, Xi’an 710049, China, and Volker L. Deringer, Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, OxfordOX1 3QR, United Kingdom.

Abstract: “Quantum-accurate computer simulations have played a central role in understanding phase-change materials (PCMs) for advanced memory technologies. However, the drastic growth in computational cost with model system size has precluded simulations on the length scales of real devices. Here we show that a single, compositionally flexible machine-learning (ML) interatomic potential model can describe the flagship Ge-Sb-Te PCMs under practical device conditions, including fully atomistic simulations of non-isothermal heating, and taking chemical disorder into account. The superior computing efficiency of the new approach enables simulations of multiple thermal cycles and delicate operations for neuro-inspired computing, namely, cumulative SET and iterative RESET. A device-scale capability demonstration (40 x 20 x 20 nm3) shows that the new ML potential can directly describe technologically relevant processes in PCM-based memory products. Our work demonstrates how atomistic ML-driven simulations are now entering a stage where they can guide architecture design for high-performance devices.“