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.

Conventional digital computers can execute advanced operations by a sequence of elementary Boolean functions of 2 or more bits. As a result, complicated tasks such as solving a linear system or solving a differential equation require a large number of computing steps and an extensive use of memory units to store individual bits. To accelerate the execution of such advanced tasks, in-memory computing with resistive memories provides a promising avenue, thanks to analog data storage and physical computation in the memory. Here, we show that a cross-point array of resistive memory devices can directly solve a system of linear equations, or find the matrix eigenvectors. These operations are completed in just one single step, thanks to the physical computing with Ohm’s and Kirchhoff’s laws, and thanks to the negative feedback connection in the cross-point circuit. Algebraic problems are demonstrated in hardware and applied to classical computing tasks, such as ranking webpages and solving the Schrödinger equation in one step.

Brain-inspired neural networks can process information with high efficiency, thus providing a powerful tool for pattern recognition and other artificial intelligent tasks. By adopting binary inputs/outputs, neural networks can be used to perform Boolean logic operations, thus potentially surpassing complementary metal-oxide-semiconductor logic in terms of area efficiency, execution time, and computing parallelism. Here, the concept of stateful neural networks consisting of resistive switches, which can perform all logic functions with the same network topology, is introduced. The neural network relies on physical computing according to Ohm's law, Kirchhoff 's law, and the ionic migration within an output switch serving as the highly nonlinear activation function. The input and output are nonvolatile resistance states of the devices, thus enabling stateful and cascadable logic operations. Applied voltages provide the synaptic weights, which enable the convenient reconfiguration of the same circuit to serve various logic functions. The neural network can solve all two-input logic operations with just one step, except for the exclusive-OR (XOR) needing two sequential steps. 1-bit full adder operation is shown to take place with just two steps and five resistive switches, thus highlighting the high efficiencies of space, time, and energy of logic computing with the stateful neural network.

Interest in resistance switching is currently growing apace. The promise of novel high-density, low-power, high-speed nonvolatile memory devices is appealing enough, but beyond that there are exciting future possibilities for applications in hardware acceleration for machine learning and artificial intelligence, and for neuromorphic computing. A very wide range of material systems exhibit resistance switching, a number of which-primarily transition metal oxides-are currently being investigated as complementary metal-oxide-semiconductor (CMOS)-compatible technologies. Here, the case is made for silicon oxide, perhaps the most CMOS-compatible dielectric, yet one that has had comparatively little attention as a resistance-switching material. Herein, a taxonomy of switching mechanisms in silicon oxide is presented, and the current state of the art in modeling, understanding fundamental switching mechanisms, and exciting device applications is summarized. In conclusion, silicon oxide is an excellent choice for resistance-switching technologies, offering a number of compelling advantages over competing material systems.

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).