Resistive memories are outstanding electron devices that have displayed a large potential in
a plethora of applications such as nonvolatile data storage, neuromorphic computing …

This review addresses resistive switching devices operating according to the bipolar
valence change mechanism (VCM), which has become a major trend in electronic materials …

Using memristor crossbar arrays to accelerate computations is a promising approach to
efficiently implement algorithms in deep neural networks. Early demonstrations, however …

Brain-inspired computing enabled by memristors has gained prominence over the years due
to the nanoscale footprint and reduced complexity for implementing synapses and neurons …

By mimicking the highly parallel biological systems, neuromorphic hardware provides the
capability of information processing within a compact and energy-efficient platform …

We propose a stochastic model for a memristive system by generalizing known approaches
and experimental results. We validate our theoretical model by experiments carried out on a …

The impact of a series resistor (RS) on the variability and endurance performance of
memristor was studied in the TaOx memristive system. A dynamic voltage divider between …

A major obstacle for the implementation of redox-based memristive memory or logic
technology is the large cycle-to-cycle and device-to-device variability. Here, we use …

Current deep learning approaches have been very successful using convolutional neural
networks trained on large graphical-processing-unit-based computers. Three limitations of …

Diverse models have been proposed over the past years to explain the exhibiting behavior
of memristors, the fourth fundamental circuit element. The models varied in complexity …