Related papers: TiO2 based Nanostructured Memristor for RRAM and N…
The operation of a novel nonvolatile memory device based on a conductive ferroelectric/non-ferroelectric thin film multilayer stack is simulated numerically. The simulation involves the self-consistent steady state solution of Poisson's…
Monolithic three-dimensional integration of memory and logic circuits could dramatically improve performance and energy efficiency of computing systems. Some conventional and emerging memories are suitable for vertical integration,…
Neuromorphic architectures mimicking biological neural networks have been proposed as a much more efficient alternative to conventional von Neumann architectures for the exploding compute demands of AI workloads. Recent neuroscience theory…
Resistive random access memory (RRAM) is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to…
Analysis of time-series data allows to identify long-term trends and make predictions that can help to improve our lives. With the rapid development of artificial neural networks, long short-term memory (LSTM) recurrent neural network (RNN)…
The first stage of tactile sensing is data acquisition using tactile sensors and the sensed data is transmitted to the central unit for neuromorphic computing. The memristive crossbars were proposed to use as synapses in neuromorphic…
We report a memory resistance (memristor) behavior with nonlinear current-voltage characteristics and bipolar hysteretic resistance switching in the nanocolumnar manganite (LSMO) films. The switching from a high (HRS) to a low (LRS)…
The ever-increasing amount of data from ubiquitous smart devices fosters data-centric and cognitive algorithms. Traditional digital computer systems have separate logic and memory units, resulting in a huge delay and energy cost for…
Neuromorphic computing promises revolutionary improvements over conventional systems for applications that process unstructured information. To fully realize this potential, neuromorphic systems should exploit the biomimetic behavior of…
Memristive switching in polycrystalline materials is widely attributed to the formation and rupture of conducting filaments, believed to be mediated by oxygen-vacancy redistribution. The underlying atomic-scale processes are still unknown,…
Recently, Sun et al reported that striking memory effects had been clearly observed in their new experiments on an interacting nanoparticle system [1]. They claimed that the phenomena evidenced the existence of a spin-glass-like phase and…
Neural processing on devices and circuits is fast becoming a popular approach to emulate biological neural networks. Elaborate CMOS and memristive technologies have been employed to achieve this, including chalcogenide-based in-memory…
Technology based on memristors, resistors with memory whose resistance depends on the history of the crossing charges, has lately enhanced the classical paradigm of computation with neuromorphic architectures. However, in contrast to the…
Machine learning imitates the basic features of biological neural networks to efficiently perform tasks such as pattern recognition. This has been mostly achieved at a software level, and a strong effort is currently being made to mimic…
The dynamics of memristive device in response to neuron-like signals and coupling electronic neurons via memristive device has been investigated theoretically and experimentally. The simplest experimental system consists of electronic…
Brain-inspired computing has the potential to revolutionise the current von Neumann architecture, advancing machine learning applications. Signal transmission in the brain relies on voltage-gated ion channels, which exhibit the electrical…
Layered two-dimensional (2D) semiconductors have shown enhanced ion migration capabilities along their van der Waals (vdW) gaps and on their surfaces. This effect can be employed for resistive switching (RS) in devices for emerging…
Memristors are nonlinear two-terminal circuit elements whose resistance at a given time depends on past electrical stimuli. Recently, networks of memristors have received attention in neuromorphic computing since they can be used as a tool…
The development of novel devices for neuromorphic computing and non-traditional logic operations largely relies on the fabrication of well controlled memristive systems with functionalities beyond standard bipolar behavior and digital…
The field of neuroimaging has truly become data rich, and novel analytical methods capable of gleaning meaningful information from large stores of imaging data are in high demand. Those methods that might also be applicable on the level of…