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  1. Memristive devices can offer dynamic behaviour, analogue programmability, and scaling and integration capabilities. As a result, they are of potential use in the development of information processing and storage devices for both conventional and unconventional computing paradigms. Their memristive switching processes originate mainly from the modulation of the number and position of structural defects or compositional impurities—what are commonly referred to as imperfections. While the underlying mechanisms and potential applications of memristors based on traditional bulk materials have been extensively studied, memristors based on van der Waals materials have only been considered more recently. Here we examine imperfection-enabled memristive switching in van der Waals materials. We explore how imperfections— together with the inherent physicochemical properties of the van der Waals materials—create different switching mechanisms, and thus provide a range of opportunities to engineer switching behaviour in memristive devices. We also discuss the challenges involved in terms of material selection, mechanism investigation and switching uniformity control, and consider the potential of van der Waals memristors in system-level implementations of efficient computing technologies. 
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    Free, publicly-accessible full text available July 17, 2024
  2. Free, publicly-accessible full text available March 30, 2024
  3. In this work, the effect of rapid thermal annealing (RTA) temperature on the ferroelectric polarization in zirconium-doped hafnium oxide (HZO) was studied. To maximize remnant polarization (2P r ), in-plane tensile stress was induced by tungsten electrodes under optimal RTA temperatures. We observed an increase in 2P r with RTA temperature, likely due to an increased proportion of the polar ferroelectric phase in HZO. The HZO capacitors annealed at 400°C did not exhibit any ferroelectric behavior, whereas the HZO capacitors annealed at 800°C became highly leaky and shorted for voltages above 1 V. On the other hand, annealing at 700 °C produced HZO capacitors with a record-high 2P r of ∼ 64 μ C cm −2  at a relatively high frequency of 111 kHz. These ferroelectric capacitors have also demonstrated impressive endurance and retention characteristics, which will greatly benefit neuromorphic computing applications. 
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  4. Abstract

    Different from nonvolatile memory applications, neuromorphic computing applications utilize not only the static conductance states but also the switching dynamics for computing, which calls for compact dynamical models of memristive devices. In this work, a generalized model to simulate diffusive and drift memristors with the same set of equations is presented, which have been used to reproduce experimental results faithfully. The diffusive memristor is chosen as the basis for the generalized model because it possesses complex dynamical properties that are difficult to model efficiently. A data set from statistical measurements on SiO2:Ag diffusive memristors is collected to verify the validity of the general model. As an application example, spike‐timing‐dependent plasticity is demonstrated with an artificial synapse consisting of a diffusive memristor and a drift memristor, both modeled with this comprehensive compact model.

     
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  6. Abstract

    Threshold switches with Ag or Cu active metal species are volatile memristors (also termed diffusive memristors) featuring spontaneous rupture of conduction channels. The temporal dynamics of the conductance evolution is closely related to the electrochemical and diffusive dynamics of the active metals which could be modulated by electric field strength, biasing duration, temperature, and so on. Microscopic pictures by electron microscopy and quantitative thermodynamics modeling are examined to give insights into the underlying physics of the switching. Depending on the time scale of the relaxation process, such devices find a variety of novel applications in electronics, ranging from selector devices for memories to synaptic devices for neuromorphic computing.

     
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