Conductive bridge memory, in which the switching results from the electrochemical growth and dissolution of a metallic filament (typically Cu or Ag), has been shown to emulate basic neuromorphic computing functions, such as spike-timing dependent plasticity and short and long-term potentiation [1-2]. However, the integration of these devices in high-density neural networks requires low power consumption, with stable bipolar switching at currents of less than 10μA. This has proved to be a key challenge in the development of solid-state synaptic devices as operation in low current regimes has been shown to lead to large resistance fluctuations and reduced device performance . A more detailed understanding of the filamentary resistive switching mechanisms is required for the realisation of functional devices.
The devices employed in this study consisted of insulator-metal (MIM) capacitor structures, comprising: Pt (25 nm)/Ag(30 nm) /HfO2 (20 nm)/Pt 50 nm. The evolution of switching behaviour was examined as a function of compliance current ranging from 1 μA to 5 mA. A transition from volatile to bipolar memory switching was observed for devices formed with compliance currents in the order of 100 μA. These results are shown to be consistent with a model in which low compliance currents lead to thin, unstable filaments, and high compliance currents lead to thicker, more stable filaments, with the transition from volatile to non-volatile behaviour determined by the relative thermal stability of the filament (i.e. the Rayleigh instability criteria).
The applicability of an analytical model of non-volatile switching presented in  is also extended to HfO2 based memory devices. This analytical model calculates the evolution of a tunnelling gap between the filament and active electrode during a triangular voltage sweep. A quantitative prediction of device variation between the high and low resistance states is important for the realisation power efficient spike-timing dependent plasticity. These findings provide an important insight into the influence of the electroforming process on switching behaviours and device stabilisation for emerging applications in solid state synapses.
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