Review demonstrates structural engineering of Ge-Sb-Te alloys via Raman and X-ray diffraction, highlighting pathways to energy-efficient neuromorphic memory devices.
Ge-Sb-Te (GST)-based phase-change memory (PCM) has emerged as a leading candidate for next-generation nonvolatile memory and neuromorphic computing applications due to its fast switching, high endurance, scalability, and multilevel data-storage capability. Raman spectroscopy and X-ray diffraction (XRD) are widely used to investigate GST materials, providing insights into atomic bonding, crystal structure, phase transitions, vacancy ordering, and lattice strain. This review systematically analyzes the transformation of Raman and XRD not only from being a structural analysis tool but also to being a highly useful design tool for improving the composition of GST, including Ge 2 Sb 2 Te 5 (GST225), Ge-doped GST, Ge 4 Sb 6 Te 7 (GST467) nanocomposite, interfacial phase-change memory (iPCM), nanocomposite, and carbon nanotube (CNT)-based PCM device. The synergistic applications of Raman spectroscopy and XRD in tracking crystallization paths, defect development, grain growth, interface quality, and the structure-property relations that determine program energy, switching current, endurance, resistance drift, and multi-level conductance stability are addressed. This review additionally highlights the direct links between the Raman/XRD-assisted structural engineering and neuromorphic computing by relating the structure to the synaptic weight stability, analog conductance tuning, and energy-efficient in-memory computing. Present-day debates on the switching mechanism of iPCM and the stability of drift-free PCM alloys over long periods of time have been thoroughly considered. The key future directions of investigation have been pointed out, such as using operando Raman spectroscopy and in-situ X-ray diffraction. In summary, this review shows that a combination of Raman spectroscopy and X-ray diffraction is essential to developing new efficient phase change materials based on GST.
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Bhatnagar et al. (2026) studied this question.
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