Silver/copper-filament-based resistive switching memory relies on the formation and disruption of a metallic conductive filament (CF) with relatively large surface-to-volume ratio. The nanoscale CF can spontaneously break after formation, with a lifetime ranging from few microseconds to several months, or even years. Controlling and predicting the CF lifetime enables device engineering for a wide range of applications, such as non-volatile memory for data storage, tunable short/long term memory for synaptic neuromorphic computing, and fast selection devices for crosspoint arrays. However, conflictive explanations for the CF retention process are being proposed. Here we show that the CF lifetime can be described by a universal surface-limited self-diffusion mechanism of disruption of the metallic CF. The surface diffusion process provides a new perspective of ion transport mechanism at the nanoscale, explaining the broad range of reported lifetimes, and paving the way for material engineering of resistive switching device for memory and computing applications.

We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

Resistive switching memory (RRAM) is among the most mature technologies for next generation storage class memory with low power, high density, and improved performance. The biggest challenge toward industrialization of RRAM is the large variability and noise issues, causing distribution broadening which affects retention even at room temperature. Noise and variability can be addressed by enlarging the resistance window between lowresistance state and high-resistance state, which requires a proper engineering of device materials and electrodes. This paper presents an RRAM device technology based on silicon oxide (SiO x ), showing high resistance window thanks to the high bandgap in the silicon oxide. Endurance, retention, and variability show excellent performance, thus supporting SiO x as a strong active material for developing future generation RRAMs.Index Terms-Cross point array, memory reliability, nonvolatile memory technology, resistive switching memory (RRAM), silicon oxide, storage class memory (SCM).

We compare SiOx and HfO2 ReRAM technologies with fixed C and Ti electrodes to understand the role played by the switching layer.

Resistive switching memory (RRAM) is among the most promising technologies for storage class memory (SCM) and embedded nonvolatile memory (eNVM). Feasibility of RRAM as SCM and/or embedded memory requires large on/off ratio, good endurance, high retention, and the availability of a robust select element for crossbar array integration. This work presents Ti/SiOx RRAM with high on/off ratio (>104), good endurance (>107), high uniformity and strong retention (260°C for 1 hour), thanks to the high SiOx band gap. Ag/SiOx devices show volatile switching with high on/off ratio (> 107) and bidirectional operation applicable to select devices in crossbar arrays