Unlocking device-scale atomistic modelling of phase-change memory materials

TL;DR

This paper presents a universal machine learning model capable of atomistically simulating phase-change memory materials at device scales, enabling realistic, quantum-accurate simulations for memory device design and operation.

Contribution

A novel, compositionally flexible ML potential model that accurately describes PCM materials under real device conditions at a scale suitable for device-level simulations.

Findings

ML model accurately simulates Ge-Sb-Te PCMs

Enables atomistic simulations of thermal cycles and operations

Demonstrates device-scale simulation of PCM memory processes

Abstract

Quantum-accurate computer simulations play a central role in understanding phase-change materials (PCMs) for advanced memory technologies. However, direct quantum-mechanical simulations are necessarily limited to simplified models, containing no more than a few hundred or a thousand atoms. Machine learning (ML) based potential models that are "trained" on quantum-mechanical data are an emerging alternative approach, currently evolving from highly specialised to more widely applied simulation tools. Here we show that a universal, compositionally flexible ML model can describe a wide range of flagship Ge-Sb-Te PCMs under real device conditions, including non-isothermal heating and chemical disorder which are relevant for memory applications. The speed of the ML model enables atomistic 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. In a wider context, our work demonstrates how ML-driven materials simulations are now entering a stage where they can guide architecture design for high-performance electronic devices.