Konstantinou Konstantinos, Mocanu Felix C, Lee Tae-Hoon, Elliott Stephen R
Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
Nat Commun. 2019 Jul 11;10(1):3065. doi: 10.1038/s41467-019-10980-w.
Understanding the relation between the time-dependent resistance drift in the amorphous state of phase-change materials and the localised states in the band gap of the glass is crucial for the development of memory devices with increased storage density. Here a machine-learned interatomic potential is utilised to generate an ensemble of glass models of the prototypical phase-change alloy, GeSbTe, to obtain reliable statistics. Hybrid density-functional theory is used to identify and characterise the geometric and electronic structures of the mid-gap states. 5-coordinated Ge atoms are the local defective bonding environments mainly responsible for these electronic states. The structural motif for the localisation of the mid-gap states is a crystalline-like atomic environment within the amorphous network. An extra electron is trapped spontaneously by these mid-gap states, creating deep traps in the band gap. The results provide significant insights that can help to rationalise the design of multi-level-storage memory devices.
理解相变材料非晶态下随时间变化的电阻漂移与玻璃带隙中的局域态之间的关系，对于开发具有更高存储密度的存储器件至关重要。在此，利用机器学习的原子间势生成典型相变合金GeSbTe的玻璃模型系综，以获得可靠的统计数据。采用杂化密度泛函理论来识别和表征带隙中间态的几何和电子结构。五配位的Ge原子是主要负责这些电子态的局部缺陷键合环境。带隙中间态局域化的结构 motif 是无定形网络内类似晶体的原子环境。一个额外的电子被这些带隙中间态自发捕获，在带隙中产生深陷阱。这些结果提供了重要的见解，有助于合理设计多级存储存储器。
