Recent progress in performance optimization of Sb-Te materials for data storage by doping modification and multilayer structure

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Abstract

In the area of artificial intelligence, information storage and processing have become more and more important for the industrial production and civil life. Phase change memory has been considered as an excellent candidate for the next generation of nonvolatile memory because of its fast speed, low power consumption, long durability and high-density information storage. In order to meet the demands of the times, people have never given up the investigation on the performance improvement of phase change memory, which heavily depends on the physical properties of phase change materials. Among the pseudo-binary GeTe-Sb2Te3 compounds, Sb-Te system exhibits the excellent amorphous stability due to its stable rock-salt phase with octahedral coordination, and also has an ultrafast operating speed owing to the presence of Sb, which has made it become a research hotspot. Unfortunately, the comprehensive performance of pure Sb-Te system cannot fully meet the commercial requirements. Therefore, it is emergency and necessary to improve the performance of Sb-Te materials via the various optimization methods. This review summarizes the recent progress of doping modification and stacking multilayer to improve the physical properties of Sb-Te alloy, including thermal and electrical properties. Also, the selection criteria of dopant, the related phase change mechanism of doping and stacking has been analyzed. The latest research of Sb-Te system will provide the extremely important and supremely meaningful reference for the design of novel phase change materials to achieve the storage class memory application and neuromorphic computation in the near future.

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Zhou, X., Wu, W., Gu, H., Zhang, P., Fu, B., Li, Y., … Zhai, J. (2025, November 1). Recent progress in performance optimization of Sb-Te materials for data storage by doping modification and multilayer structure. Materials Science in Semiconductor Processing. Elsevier Ltd. https://doi.org/10.1016/j.mssp.2025.109756

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