Phase-change materials (PCMs) have demonstrated a wide range of potential applications ranging from electronic memories to photonic devices. These applications are enabled by the unconventional portfolio of properties that characterizes crystalline PCMs. Here, we address the origin of these unusual properties and how they are related to the application potential of these materials. Evidence will be presented that the properties are related to an unconventional bonding mechanism. Employing a novel map, which separates solids according to the number of electrons transferred and shared between adjacent atoms, it is shown that PCMs occupy a well-defined region. Depicting physical properties such as the optical dielectric constant as the third dimension in the map reveals systematic property trends. Such trends can be utilized to unravel the origins of the unconventional materials properties or alternatively, as a means to optimize them.

High-current switching performance of ovonic threshold switching (OTS) selectors have successfully enabled the commercialization of high-density three-dimensional (3D) stackable phase-change memory in Intel’s 3D Xpoint technology. This bridges the huge performance gap between dynamic random access memory (DRAM) and Flash. Similar to phase-change memory, OTS uses chalcogenide-based materials, but whereas phase-change memory reversibly switches between a high-resistance amorphous phase and a low-resistance crystalline phase, OTS freezes in the amorphous phase. In this article, we review recent developments in OTS materials and their performance in devices, especially current density and selectivity. Advantages and challenges of OTS devices in the integration with the phase-change memory are discussed. We introduce the evolution of theoretical models for explaining the OTS behavior, including thermal runaway, field-induced nucleation, and generation/recombination of charge carriers.

The rapidly growing demand for data storage and processing, driven by artificial intelligence (AI) and other data-intensive applications, is posing a serious challenge for current computing devices based on the von Neumann architecture. For every calculation, data sets need to be shuffled sequentially between the processor, and multiple memory and storage units through bandwidth-limited and energy-inefficient interconnects, typically causing 40% power wastage. Phase-change materials (PCMs) show great promise to break this bottleneck by enabling nonvolatile memory devices that can optimize the complex memory hierarchy, and neuro-inspired computing devices that can unify computing with storage in memory cells. The articles in this issue of MRS Bulletin highlight recent breakthroughs in the fundamental materials science, as well as electronic and photonic implementations of these novel devices based on PCMs.

The exploitation of phase-change materials (PCMs) in diverse technological applications can be greatly aided by a better understanding of the microscopic origins of their functional properties. Over the last decade, simulations based on electronic-structure calculations within density functional theory (DFT) have provided useful insights into the properties of PCMs. However, large simulation cells and long simulation times beyond the reach of DFT simulations are needed to address several key issues of relevance for the performance of devices. One way to overcome the limitations of DFT methods is to use machine learning (ML) techniques to build interatomic potentials for fast molecular dynamics simulations that still retain a quasi-ab initio accuracy. Here, we review the insights gained on the functional properties of the prototypical PCM GeTe by harnessing such interatomic potentials. Applications and future challenges of the ML techniques in the study of PCMs are also outlined.

The cycling endurance of phase-change memory is one of the last hurdles to overcome to enable its adoption in the larger market for persistent memory products. Phase-change memory cycling endurance failures, whether they are stuck-SET (caused by elemental segregation) or stuck-RESET (caused by void formation), are caused by atomic migration. Various driving forces responsible for the atomic migration have been identified, such as hole-wind force, electrostatic force, and crystallization-induced segregation. We introduce several strategies to improve cycling endurance based on an understanding of driving forces and interactions among them. Utilizing some of these endurance-improving techniques, record-high phase-change memory cycling endurance at around 1012 cycles has been recently reported using a confined phase-change memory cell with a metallic liner.

Driven by the rapid rise of silicon photonics, optical signaling is moving from the realm of long-distance communications to chip-to-chip, and even on-chip domains. If on-chip signaling becomes optical, we should consider what more we might do with light than just communicate. We might, for example, set goals for the storing and processing of information directly in the optical domain. Doing this might enable us to supplement, or even surpass, the performance of electronic processors, by exploiting the ultrahigh bandwidth and wavelength division multiplexing capabilities offered by optics. In this article, we show how, by using an integrated photonics platform that embeds chalcogenide phase-change materials into standard silicon photonics circuits, we can achieve some of these goals. Specifically, we show that a phase-change integrated photonics platform can deliver binary and multilevel memory, arithmetic and logic processing, as well as synaptic and neuronal mimics for use in neuromorphic, or brain-like, computing—all working directly in the optical domain.

Impaired cognitive functioning constitutes an important symptom of major depressive disorder (MDD), potentially associated with elevated cortisol levels. Adverse childhood experiences (ACE) enhance the risk for MDD and can contribute to disturbances in the stress systems, including cortisol and cognitive functions. In healthy participants, cortisol administration as well as acute stress can affect cognitive performance. In the current study, we tested cognitive performance in MDD patients with (N = 32) and without (N = 52) ACE and healthy participants with (N = 22) and without (N = 37) ACE after psychosocial stress induction (Trier Social Stress Test, TSST) and a control condition (Placebo-TSST). MDD predicted lower performance in verbal learning and both selective and sustained attention, while ACE predicted lower performance in psychomotoric speed and working memory. There were no interaction effects of MDD and ACE. After stress, MDD patients were more likely to show lower performance in working memory as well as in selective and sustained attention compared with participants without MDD. Individuals with ACE were more likely to show lower performance in verbal memory after stress compared with individuals without ACE. Our results indicate negative effects of MDD and ACE on distinct cognitive domains. Furthermore, MDD and/or ACE seem to enhance susceptibility for stress-related cognitive impairments.