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, Gesub2/subSbsub2/subTesub5/sub, 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.
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机译：理解相变材料的非晶态的时间依赖性电阻漂移与玻璃带隙中的局部状态之间的关系对于具有增加的存储密度的存储器装置的开发至关重要。这里，机器学习的内部电位用于产生原型相变合金的玻璃模型的集合，Ge 2 sb 2 te 5 ，获得可靠的统计数据。混合密度功能理论用于识别和表征中隙状态的几何和电子结构。 5协调的GE原子是本地有缺陷的粘合环境，主要负责这些电子国家。用于定位中间隙状态的结构基序是非晶网络内的结晶原子环境。额外的电子被这些中隙状态自发捕获，在带隙中产生深陷阱。结果提供了显着的见解，可以帮助合理化多级存储存储器设备的设计。
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