Genesis and Effects of Swapping Bilayers in Hexagonal GeSb<sub>2</sub>Te<sub>4</sub>

Wang, Jiangjing; Wang, Jun; Du, Hongchu; Lu, Lu; Schmitz, Peter; Reindl, Johannes; Mio, Antonio Massimiliano; Jia, Chun‐Lin; Ma, Evan; Mazzarello, Riccardo; Wuttig, Matthias; Zhang, Wei

doi:10.1021/acs.chemmater.8b01900

Cited by 40 publications

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“…However, many stacking faults were frequently observed in the sputtered samples (Figure c). For instance, some twin‐like structures with inversed stacking sequence could be observed, similar to those in GST, though the density of such defects is much lower in the Sb 2 Te 3 samples. Septuple‐layer (SL) nanolamella was more frequently observed, and the size of these nanolamella varies, as indicated by the orange boxes in Figure c and Figure S2, Supporting Information.…”

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confidence: 84%

“…Instead of unconstrained phase transitions between the amorphous and crystalline state, short‐range phase transitions through local rearrangements, such as layer‐switching between two‐layered crystalline states of GST, are proposed for iPCM. Although this switching mechanism is still under debate, layer‐switching has been shown to occur in layer‐structured GST through swapped bilayers . The intermixing of Sb and Te has been proven to be essential for the stabilization of the swapped bilayers .…”

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confidence: 99%

“…Although this switching mechanism is still under debate, layer‐switching has been shown to occur in layer‐structured GST through swapped bilayers . The intermixing of Sb and Te has been proven to be essential for the stabilization of the swapped bilayers . However, it has not yet been clarified whether Ge plays any significant role in the genesis and motion of the swapped bilayers.…”

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confidence: 99%

“…These bilayer defects appear in series and are found frequently in the sputtered samples. They were suggested to be the key element for layer‐switching in iPCMs by triggering the reconfiguration of vdW‐like gaps …”

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Layer‐Switching Mechanisms in Sb₂Te₃

Wang

et al. 2019

Physica Rapid Research Ltrs

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Interfacial phase‐change memory (iPCM) based on layer‐structured Ge‐Sb‐Te crystals has been recently proposed, offering an energy‐efficient implementation of nonvolatile memory cells and supplementing the development of Ge‐Sb‐Te‐based phase‐change random access memories (PRAMs). Although the working principle of iPCM is still under debate, it is believed that layer‐switching plays a role in the switching process between the low‐resistance and high‐resistance states of iPCM memory cells. However, the role of Ge in forming swapped bilayers—the key elements for layer‐switching—is not yet clarified. This work manages to achieve layer‐switching in Sb2Te3 thin films by manipulating the formation of bilayer defects using magnetron sputtering and post‐thermal annealing. By combining scanning transmission electron microscopy (STEM) experiments with density functional theory (DFT) calculations, the essential role of Sb‐Te intermixing is elucidated in stabilizing swapped bilayers at a low energy cost. In situ STEM experiments provide a real‐time and real‐space view of dynamical reconfiguration of van der Waals‐like gaps in Sb2Te3 thin films under electron‐beam irradiation. The results show that the Ge atoms are not necessary for the formation and motion of swapped bilayers, providing atomic insights on the layer‐switching mechanism in layer‐structured binary and ternary group V‐ and IV–V‐tellurides for memory applications.

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confidence: 84%

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confidence: 99%

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confidence: 99%

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Layer‐Switching Mechanisms in Sb₂Te₃

Wang

et al. 2019

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“…[28] Open questions in applying future generations of ML potentials to PCMs fall into two categories, defined by the chemical composition. [133] Besides the bulk phases ( Figure 3c) and nanowires (Figure 3d) discussed in this Progress Report, it would be highly valuable to understand more thoroughly the atomistic structure and behavior of superlattice structures, [134] continuing along the lines of existing DFT studies [135] but now on larger length scales. The nature of defect states in amorphous Ge 2 Sb 2 Te 5 has been studied very recently using a large number of structural models generated in ML-driven simulations.…”

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confidence: 99%

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

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In pursuit of this goal, atomic-scale computer simulations have long been a central approach, and two major families of methods are routinely used today. On the one hand, there are quantum-mechanical simulations, in which we solve Schrödinger's equation for the electronic structure of molecular and periodic systems, most widely based on density-functional theory (DFT). [6][7][8] These methods provide (largely) reliable results for structural models of materials that normally contain a few tens or hundreds of atoms. State-of-the-art DFT methods can be applied to many material classes, and they are increasingly used for high-throughput screening and "in silico" (computer-based) design of materials: new compositions and previously unknown structures have been identified in DFT searches and subsequently experimentally realized. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. These simulations grant access to larger time and length scales, reaching system sizes of up to hundreds of thousands of atoms. [12] In parameterizing these potentials, a certain physical form of the atomic interactions is assumed, often in terms of bond distances, angles, and so on, and physical properties such as equilibrium lattice parameters or elastic constants enter the fitting of the potential. For this reason, such potentials are often called "empirical." They are several orders of magnitude faster than DFT, but necessarily less accurate and less easily transferable.In this Progress Report, we highlight recent developments in "machine-learned" interatomic potentials, which represent a rapidly growing field that promises to do away with the aforementioned trade-offs. Over the last year, there has been a surge of interest in machine learning (ML) methodology: part of it is due to the dramatic growth of ML throughout the scientific disciplines, and part of it is due to tangible success stories of ML-based interatomic potentials that are now beginning to emerge. We will argue that this is an exciting development with very practical implications, currently on the verge of moving from a somewhat specialized new technology to everyday applicability, poised to enhance and complement the communities' existing strengths in computational materials modeling. We will show selected applications of ML potentials to problems in materials science, discuss the current limitations (and possible pitfalls), and outline what we expect to be interesting directions for the development of the field in the coming years.Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materi...

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Phase‐Change Superlattice Materials toward Low Power Consumption and High Density Data Storage: Microscopic Picture, Working Principles, and Optimization

Chen

Wang

et al. 2018

Adv Funct Materials

133

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To meet the requirement of big data era and neuromorphic computations, nonvolatile memory with fast speed, high density, and low power consumption is urgently needed. As an emerging technology, phase-change memory is a promising candidate to solve this problem. However, the drawback of the high power consumption hinders their applications. Most recently, a new phase-change material of [(GeTe) x /(Sb 2 Te 3 ) y ] n superlattice attracts intensive attentions owing to its ultralow power consumption comparing with conventional phase-change memory devices. Many studies on this new material have been reported. However, there still lacks a comprehensive and unified understanding of its atomic picture and working mechanism. This article at first summarizes the broad applications for phase-change materials. Then, the major progresses of phase-change superlattices to understand the microscopic structure and working principles for data storage are discussed. Strategies on material optimizations to further enhance device performances are proposed. Finally, an outlook on new applications with these advanced superlattice materials is suggested.

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Genesis and Effects of Swapping Bilayers in Hexagonal GeSb₂Te₄

Cited by 40 publications

References 53 publications

Layer‐Switching Mechanisms in Sb₂Te₃

Layer‐Switching Mechanisms in Sb₂Te₃

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Phase‐Change Superlattice Materials toward Low Power Consumption and High Density Data Storage: Microscopic Picture, Working Principles, and Optimization

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Genesis and Effects of Swapping Bilayers in Hexagonal GeSb2Te4

Cited by 40 publications

References 53 publications

Layer‐Switching Mechanisms in Sb2Te3

Layer‐Switching Mechanisms in Sb2Te3

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Phase‐Change Superlattice Materials toward Low Power Consumption and High Density Data Storage: Microscopic Picture, Working Principles, and Optimization

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Genesis and Effects of Swapping Bilayers in Hexagonal GeSb₂Te₄

Layer‐Switching Mechanisms in Sb₂Te₃

Layer‐Switching Mechanisms in Sb₂Te₃