论文标题
解锁设备尺度的原子模型对相位变化记忆材料的建模
Unlocking device-scale atomistic modelling of phase-change memory materials
论文作者
论文摘要
量子精确的计算机模拟在理解高级内存技术的相变材料（PCM）中起着核心作用。但是，直接的量子力学模拟必须限于简化模型，其中包含不超过几百或一千个原子。在量子力学数据上“训练”的基于机器学习（ML）的潜在模型是一种新兴的替代方法，目前从高度专业化到更广泛应用的模拟工具发展。在这里，我们表明，在实际设备条件下（包括与记忆应用相关的非等热加热和化学障碍），通用的，构图柔性的ML模型可以描述广泛的旗舰GE-SB-TE PCM。 ML模型的速度可以对多个热循环和微妙的操作进行原子模拟，以进行神经启发的计算，即累积集和迭代重置。设备尺度的能力演示（40 x 20 x 20 nm3）表明，新的ML电位可以直接描述基于PCM的内存产品中的技术相关过程。在更广泛的背景下，我们的工作表明了ML驱动的材料模拟现在如何进入一个阶段，他们可以指导高性能电子设备的建筑设计。
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.
