The authors report on 7Li, 19F, and 1H pulsed field gradient NMR measurements of 26 organosilyl nitrile solvent-based electrolytes of either lithium bis(trifluorosulfonyl)imide (LiTFSI) or lithium hexafluorophosphate. Lithium transport numbers (as high as 0.50) were measured and are highest in the LiTFSI electrolytes. The authors also report on solvent blend electrolytes of fluoroorganosilyl (FOS) nitrile solvent mixed with ethylene carbonate (EC) and diethyl carbonate. Solvent diffusion measurements on an electrolyte with 6% FOS suggest both the FOS and EC solvate the lithium cation. By comparing lithium transport and transference numbers, the authors find less ion pairing in FOS nitrile carbonate blend electrolytes and difluoroorganosilyl nitrile electrolytes.

While fast-switching rewritable nonvolatile memory units based on phase-change materials (PCMs) are already in production at major technology companies such as Intel (16–64 GB chips are currently available), an in-depth understanding of the physical factors that determine their success is still lacking. Recently, we have argued for a liquid-phase metal-to-semiconductor transition (M-SC), located not far below the melting point, Tm, as essential. The M-SC is itself a consequence of atomic rearrangements that are involved in a fragile-to-strong viscosity transition that controls both the speed of crystallization and the stabilization of the semiconducting state. Here, we review past work and introduce a new parameter, the “metallicity” (inverse of the average Pauling electronegativity of a multicomponent alloy). When Tm-scaled temperatures of known M-SCs of Group IV, V, and VI alloys are plotted against their metallicities, the curvilinear plot leads directly to the composition zone of all known PCMs and the temperature interval below Tm, where the transition should occur. The metallicity concept could provide guidance for tailoring PCMs.

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.

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.

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.