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Phase-change memory (PCM) is an emerging non-volatile memory technology that is based on the reversible and rapid phase transition between the amorphous and crystalline phases of certain phase-change materials. The ability to alter the conductance levels in a controllable way makes PCM devices particularly well-suited for synaptic realizations in neuromorphic computing. A key attribute that enables this application is the progressive crystallization of the phase-change material and subsequent increase in device conductance by the successive application of appropriate electrical pulses. There is significant inter- and intra-device randomness associated with this cumulative conductance evolution, and it is essential to develop a statistical model to capture this. PCM also exhibits a temporal evolution of the conductance values (drift), which could also influence applications in neuromorphic computing. In this paper, we have developed a statistical model that describes both the cumulative conductance evolution and conductance drift. This model is based on extensive characterization work on 10 000 memory devices. Finally, the model is used to simulate the supervised training of both spiking and non-spiking artificial neuronal networks. [ABSTRACT FROM AUTHOR]

There is a significant need to build efficient non-von Neumann computing systems for highly data-centric artificial intelligence related applications. Brain-inspired computing is one such approach that shows significant promise. Memory is expected to play a key role in this form of computing and, in particular, phase-change memory (PCM), arguably the most advanced emerging non-volatile memory technology. Given a lack of comprehensive understanding of the working principles of the brain, brain-inspired computing is likely to be realized in multiple levels of inspiration. In the first level of inspiration, the idea would be to build computing units where memory and processing co-exist in some form. Computational memory is an example where the physical attributes and the state dynamics of memory devices are exploited to perform certain computational tasks in the memory itself with very high areal and energy efficiency. In a second level of brain-inspired computing using PCM devices, one could design a co-processor comprising multiple cross-bar arrays of PCM devices to accelerate the training of deep neural networks. PCM technology could also play a key role in the space of specialized computing substrates for spiking neural networks, and this can be viewed as the third level of brain-inspired computing using these devices. [ABSTRACT FROM AUTHOR]

In spite of decades of research, the details of electrical transport in phase-change materials are still debated. In particular, the so-called threshold switching phenomenon that allows the current density to increase steeply when a sufficiently high voltage is applied is still not well understood, even though there is wide consensus that threshold switching is solely of electronic origin. However, the high thermal efficiency and fast thermal dynamics associated with nanoscale phase-change memory (PCM) devices motivate us to reassess a thermally assisted threshold switching mechanism, at least in these devices. The time/temperature dependence of the threshold switching voltage and current in doped Ge2Sb2Te5 nanoscale PCM cells was measured over 6 decades in time at temperatures ranging from 40 °C to 160 °C. We observe a nearly constant threshold switching power across this wide range of operating conditions. We also measured the transient dynamics associated with threshold switching as a function of the applied voltage. By using a field- and temperature-dependent description of the electrical transport combined with a thermal feedback, quantitative agreement with experimental data of the threshold switching dynamics was obtained using realistic physical parameters. [ABSTRACT FROM AUTHOR]

Electrical transport in chalcogenide-based phase change materials is an active area of research owing to the prominent role played by these materials in the field of information technology. Here, we present transport measurements (IV curves) obtained on line-cells of as-deposited amorphous phase change materials (Ge2Sb2Te5, GeTe, Ag4In3Sb66Te27) over a wide voltage and temperature range (300K to 160 K). The well defined geometry of our devices enables a description of the transport behavior in terms of conductivity vs. electric field. At higher temperatures (300K≥T≥220 K) and low to intermediate fields (F<20 V/µm), the data can be described within the framework of a previously developed model, which is based on multiple trapping transport together with 3D Poole-Frenkel emission from a two-center Coulomb potential. Based on this model, we observe a temperature dependence of the inter-trap distance, which we can relate to a temperature dependence in the occupation of the defect creating the Coulomb potential governing Poole- Frenkel emission. At higher fields and lower temperatures, the dependency of the IV curve on the electric field can be described by ln(I/I0)=(F/Fc)2. By combining this contribution with that of the Poole-Frenkel emission, we can show that the slope at high fields, Fc, is independent of temperature. We argue that models based on direct tunneling or thermally assisted tunneling from a single defect into the valence band cannot explain the observed behavior quantitatively. [ABSTRACT FROM AUTHOR]