Slow access time, high power dissipation, and a rapidly approaching scaling limit constitute roadblocks for existing nonvolatile flash memory technologies. A new family of storage devices is needed. Filamentary resistive RAM (ReRAM) offers scalability, potentially sub-10 nm, nanosecond write times and a low power profile. Importantly, applications beyond binary memories are also possible. Here, we look at aspects of the electrical response to nanosecond stimuli of intrinsic resistance switching TiN/SiOx/TiN ReRAM devices. Simple sequences of identical pulses switch devices between two or more states, leading to the possibility of simplified programmers. Impedance mismatch between the device under test and the measurement system allows us to track the electroforming process and confirm it occurs on the nanosecond timescale. Furthermore, we report behavior reminiscent of neuronal synapses (potentiation, depression, and short-term memory). Our devices therefore show great potential for integration into novel hardware neural networks.

In recent years, formidable effort has been devoted to exploring the potential of Resistive RAM (RRAM) devices to model key features of biological synapses. This is done to strengthen the link between neuro-computing architectures and neuroscience, bearing in mind the extremely low power consumption and immense parallelism of biological systems. Here we demonstrate the feasibility of using the RRAM cell to go further and to model aspects of the electrical activity of the neuron. We focus on the specific operational procedures required for the generation of controlled voltage transients, which resemble spike-like responses. Further, we demonstrate that RRAM devices are capable of integrating input current pulses over time to produce thresholded voltage transients. We show that the frequency of the output transients can be controlled by the input signal, and we relate recent models of the redox-based nanoionic resistive memory cell to two common neuronal models, the Hodgkin-Huxley (HH) conductance model and the leaky integrate-and-fire model. We employ a simplified circuit model to phenomenologically describe voltage transient generation.

We apply a three-dimensional (3D) physical simulator, coupling self-consistently stochastic kinetic Monte Carlo descriptions of ion and electron transport, to investigate switching in silicon-rich silica (SiOx) redox-based resistive random-access memory (RRAM) devices. We explain the intrinsic nature of resistance switching of the SiOx layer, and demonstrate the impact of self-heating effects and the initial vacancy distributions on switching. We also highlight the necessity of using 3D physical modelling to predict correctly the switching behavior. The simulation framework is useful for exploring the little-known physics of SiOx RRAMs and RRAM devices in general. This proves useful in achieving efficient device and circuit designs, in terms of performance, variability and reliability.

Conductive atomic force microscopy was used to etch through SiOx resistance switching devices to produce three-dimensional renderings of conductive filaments.