Ab initio molecular dynamics and materials design for embedded phase-change memory

The Ge2Sb2Te5 alloy has served as the core material in phase-change memories with high switching speed and persistent storage capability at room temperature. However widely used, this composition is not suitable for embedded memories—for example, for automotive applications, which require very high working temperatures above 300 °C. Ge–Sb–Te alloys with higher Ge content, most prominently Ge2Sb1Te2 (‘212’), have been studied as suitable alternatives, but their atomic structures and structure–property relationships have remained widely unexplored. Here, we report comprehensive first-principles simulations that give insight into those emerging materials, located on the compositional tie-line between Ge2Sb1Te2 and elemental Ge, allowing for a direct comparison with the established Ge2Sb2Te5 material. Electronic-structure computations and smooth overlap of atomic positions (SOAP) similarity analyses explain the role of excess Ge content in the amorphous phases. Together with energetic analyses, a compositional threshold is identified for the viability of a homogeneous amorphous phase (‘zero bit’), which is required for memory applications. Based on the acquired knowledge at the atomic scale, we provide a materials design strategy for high-performance embedded phase-change memories with balanced speed and stability, as well as potentially good cycling capability

Materials Screening for Disorder-Controlled Chalcogenide Crystals for Phase-Change Memory Applications

Tailoring the degree of disorder in chalcogenide phase-change materials (PCMs) plays an essential role in nonvolatile memory devices and neuro-inspired computing. Upon rapid crystallization from the amorphous phase, the flagship Ge–Sb–Te PCMs form metastable rocksalt-like structures with an unconventionally high concentration of vacancies, which results in disordered crystals exhibiting Anderson-insulating transport behavior. Here, ab initio simulations and transport experiments are combined to extend these concepts to the parent compound of Ge–Sb–Te alloys, viz., binary Sb2Te3, in the metastable rocksalt-type modification. Then a systematic computational screening over a wide range of homologous, binary and ternary chalcogenides, elucidating the critical factors that affect the stability of the rocksalt structure is carried out. The findings vastly expand the family of disorder-controlled main-group chalcogenides toward many more compositions with a tunable bandgap size for demanding phase-change applications, as well as a varying strength of spin–orbit interaction for the exploration of potential topological Anderson insulators

Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

Interatomic potentials: predicting phase transformations in zirconium Machine learning leads to a new interatomic potential for zirconium that can predict phase transformations. A team led by Hongxian Zong at Xi’an Jiaotong University, China, and Turab Lookman at Los Alamos National Laboratory, U.S.A, used a Gaussian-type machine learning approach to produce an interatomic potential that predicted phase transformations in zirconium. They expressed each atomic energy contribution via changes in the local atomic environment, such as bond length, shape, and volume. The resulting machine-learning potential successfully described pure zirconium’s physical properties. When used in molecular dynamics simulations, it predicted a zirconium phase diagram as a function of both temperature and pressure that agreed well with previous experiments and simulations. Developing learnt interatomic potentials in phase-transforming systems could help us better simulate complex systems

A density-functional study on the electronic and vibrational properties of layered antimony telluride

We present a comprehensive survey of electronic and lattice-dynamical properties of crystalline antimony telluride (Sb2Te3). In a first step, the electronic structure and chemical bonding have been investigated, followed by calculations of the atomic force constants, phonon dispersion relationships and densities of states. Then, (macroscopic) physical properties of Sb2Te3 have been computed, namely, the atomic thermal displacement parameters, the Grüneisen parameter γ, the volume expansion of the lattice, and finally the bulk modulus B. We compare theoretical results from three popular and economic density-functional theory (DFT) approaches: the local density approximation (LDA), the generalized gradient approximation (GGA), and a posteriori dispersion corrections to the latter. Despite its simplicity, the LDA shows excellent performance for all properties investigated - including the Grüneisen parameter, which only the LDA is able to recover with confidence. In the absence of computationally more demanding hybrid DFT methods, the LDA seems to be a good choice for further lattice dynamical studies of Sb2Te3 and related layered telluride materials

A density-functional study on the electronic and vibrational properties of layered antimony telluride

We present a comprehensive survey of electronic and lattice-dynamical properties of crystalline antimony telluride (Sb2Te3). In a first step, the electronic structure and chemical bonding have been investigated, followed by calculations of the atomic force constants, phonon dispersion relationships and densities of states. Then, (macroscopic) physical properties of Sb2Te3 have been computed, namely, the atomic thermal displacement parameters, the Grüneisen parameter γ, the volume expansion of the lattice, and finally the bulk modulus B. We compare theoretical results from three popular and economic density-functional theory (DFT) approaches: the local density approximation (LDA), the generalized gradient approximation (GGA), and a posteriori dispersion corrections to the latter. Despite its simplicity, the LDA shows excellent performance for all properties investigated - including the Grüneisen parameter, which only the LDA is able to recover with confidence. In the absence of computationally more demanding hybrid DFT methods, the LDA seems to be a good choice for further lattice dynamical studies of Sb2Te3 and related layered telluride materials. © 2015 IOP Publishing Ltd

Chemical understanding of resistance drift suppression in Ge-Sn-Te phase-change memory materials

The resistance drift phenomenon observed in amorphous chalcogenide phase-change materials (PCMs) hinders the development of PCM-based neuro-inspired computing devices. It has been observed that the drift in electrical resistance can be effectively reduced by substituting Ge with Sn in the prototype PCM GeTe, forming amorphous (Ge1-xSnx)Te solids. However, the atomistic and chemical origin of such drift suppression phenomenon remains unclear. In this work, we carry out thorough ab initio simulations and chemical bonding analyses of amorphous Ge-Sn-Te materials. We show that the two critical driving forces for glass relaxation in PCMs, i.e. the amount of tetrahedral motifs and the degree of Peierls distortion, are gradually reduced as Sn content increases. Such trend can be explained by the increased ionicity brought about by the Ge → Sn substitution. Our work suggests that an optimal Sn-rich GeSnTe composition could be reached for PCM-based neuro-inspired computing with ultralow resistance drift

Bonding similarities and differences between Y-Sb-Te and Sc-Sb-Te phase-change memory materials

The scandium (Sc)-alloyed Sb2Te3 phase-change alloy has recently been found to enable ultrafast crystal nucleation due to the formation of Sc-stabilized octahedral motifs in the amorphous phase, rendering cache-type phase-change memory feasible. When yttrium (Y) is added, however, non-octahedral bonding patterns form in the amorphous Sb2Te3-based network even though Y has a valence electron configuration similar to that of Sc and also forms perfect octahedral bonding environments with tellurium in the YTe crystal. Here we elucidate the origin of this difference between Sc and Y, by carrying out thorough ab initio simulations and orbital-based bonding analyses on amorphous Y-Sb-Te and Sc-Sb-Te compounds. We also demonstrate how the smooth overlap of atomic positions (SOAP) similarity kernel can be used to quantify the structural similarity of local motifs in the amorphous phase with respect to various crystalline yttrium and scandium tellurides, both in the nearest-neighbor shell and beyond. We find that the bonding contrast of Y- and Sc-centered structural motifs in amorphous Sb2Te3 stems from their parent crystals at high Te concentrations. The larger atomic radius of Y and the weaker charge transfer when bonded with Te is found to allow more Te neighbors and cause a more open bonding environment, leading to higher coordination numbers and non-octahedral environments. We discuss the implications of the different local environments for practical applications in memory devices