Role of GO and r-GO in resistance switching behavior of bilayer TiO2 based RRAM

Graphene-based resistance random access memory devices (RRAMs) have shown promise as a suitable replacement for flash memories, owing to their fast switching speed, low programming voltage, better scalability and great reliability. Furthermore, recent research works have shown bi-layer RRAM devices exhibiting better performance along the same parameters, where titania is one of the most commonly used materials. In the present work, we have studied the resistance switching behavior in a bi-layer RRAM device structure of TiO2 with graphene oxide (GO) and reduced graphene oxide (rGO). Switching mechanism in these devices has been investigated by detailed experimental characterization in conjunction with a finite element modeling (FEM) simulation. A dual conical conductive filament has been used in the present work, based on the modeling of the electroforming process carried out by FEM. It has been demonstrated that for the GO/TiO2 based hybrid RRAM device structure, GO acts as an active filament formation layer, whereas in the rGO/TiO2 bi-layer structure, rGO acts as a mere electrode

Fully memristive neural networks for pattern classification with unsupervised learning

Neuromorphic computers comprised of artificial neurons and synapses could provide a more efficient approach to implementing neural network algorithms than traditional hardware. Recently, artificial neurons based on memristors have been developed, but with limited bio-realistic dynamics and no direct interaction with the artificial synapses in an integrated network. Here we show that a diffusive memristor based on silver nanoparticles in a dielectric film can be used to create an artificial neuron with stochastic leaky integrate-and-fire dynamics and tunable integration time, which is determined by silver migration alone or its interaction with circuit capacitance. We integrate these neurons with nonvolatile memristive synapses to build fully memristive artificial neural networks. With these integrated networks, we experimentally demonstrate unsupervised synaptic weight updating and pattern classification