The threshold switching effect is considered of outmost importance for a variety of applications ranging from the reliable operation of crossbar architectures to emulating neuromorphic properties with artificial neural networks. This property is strongly believed to be associated with the rich inherit dynamics of a metallic conductive filament (CF) formation and its respective relaxation processes. Understanding the origin of these dynamics is very important in order to control the degree of volatility and design novel electronic devices. Here, we present a synergistic numerical and experimental approach in order to deal with that issue. The distribution of relaxation time is addressed through time-resolved pulse measurements whereas the entire switching behavior is modeled through a 2D dynamical model by taking into account the destructive interference of the drift/diffusion transport mechanisms and the Soret diffusion flux due to the intense local Joule heating. The proposed mechanism interprets successfully both the threshold to bipolar switching transition as well as the self-rectifying effects in SiO2-based memories. The model incorporates the effect of electrode materials on the switching pattern and provides a different perception of the ionic transport processes, shading light into the ultra-small lifetimes of the CF and explaining the different behavior of the silver or copper active materials in a conductive bridge random access memory architecture.

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The quick growth of information technology has necessitated the need for developing novel electronic devices capable of performing novel neuromorphic computations with low power consumption and a high degree of accuracy. In order to achieve this goal, it is of vital importance to devise artificial neural networks with inherent capabilities of emulating various synaptic properties that play a key role in the learning procedures. Along these lines, we report here the direct impact of a dense layer of Pt nanoparticles that plays the role of the bottom electrode, on the manifestation of the bipolar switching effect within SiO2-based conductive bridge memories. Valuable insights regarding the influence of the thermal conductivity value of the bottom electrode on the conducting filament growth mechanism are provided through the application of a numerical model. The implementation of an intermediate switching transition slope during the SET transition permits the emulation of various artificial synaptic functionalities, such as short-term plasticity, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights toward the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior.

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The growth of flexible memory devices with intrinsic neuromorphic properties is attracting tremendous attention due to the wide spread of wearable electronics. The fabrication of robust artificial synaptic structures that will have the ability to remain operational after various strain loads and perform artificial synaptic weight modulation procedures is considered the cornerstone for the development of wearable neuromorphic computing systems. Along these lines, a low-temperature Ag/SiO 2 /TiN memristive device is demonstrated on a polyethylene naphthalate substrate decorated with Pt nanoparticles (NPs) with a 5 nm average diameter. The memory devices are completely forming-free, operate at a low voltage of 500 mV, and exhibit great stability after bending numerous times under a record high mechanical strain of 4.16% without any severe degradation of the pulse endurance and retention capabilities. Concrete pieces of evidence regarding the crack suppression effect induced by the layer of Pt NPs on the robust memory performance are also provided. Moreover, a thorough analysis of the neuromorphic properties of our prototypes is presented, including the implementation of short-term plasticity effects, such as paired-pulsed facilitation and paired-pulse depression, which are of great importance for the rehearsal procedures of neurons during the learning function as well as for the linear distribution of the conductance pattern during synaptic potentiation and depression tasks. Additionally, Hebbian spike-time-dependent-plasticity responses were also recorded.