Related papers: Purely electronic nanometallic ReRAM
Memristive circuit elements constitute a cornerstone for novel electronic applications, such as neuromorphic computing, called to revolutionize information technologies. By definition, memristors are sensitive to the history of electrical…
Synaptic devices with linear high-speed switching can accelerate learning in artificial neural networks (ANNs) embodied in hardware. Conventional resistive memories however suffer from high write noise and asymmetric conductance tuning,…
Optical devices with metastable states controlled with light (optical flip-flops) are needed in data storage, signal processing and displays. Although non-volatile optical memory relying on structural phase transitions in chalcogenide…
Reversible logic has become immensely popular research area and its applications have spread in various technologies for their low power consumption. In this paper we proposed an efficient design of random access memory using reversible…
The rapid advancement of neuromorphic technology aims to address the memory wall challenge inherent in conventional von Neumann architectures. This paper critically examines current digital neuromorphic processors and their strategies to…
Complementary resistive switches (CRS) are considered as a potential solution for the sneak path problem in large-scale integration of passive crossbar resistive memory arrays. A typical CRS is composed of two bipolar memory cells that are…
Resistive random-access memories, also known as memristors, whose resistance can be modulated by the electrically driven formation and disruption of conductive filaments within an insulator, are promising candidates for neuromorphic…
Solid state ionic conductors are good candidates for the next generation of nonvolatile computer memory elements. Such devices have to show reproducible resistance switching at reasonable voltage and current values even if scaled down to…
Magnetic nanomaterials can be used in the construction of devices for information processing and memory storage. For this purpose, they have to enjoy two contradictory properties, from one side being able of keeping for long time…
With the staggering increase of edge compute applications like Internet-of-Things (IoT) and artificial intelligence (AI), the demand for fast, energy-efficient on-chip memory is growing. While the fast and mature static random-access memory…
Information technologies require entangling data stability with encryption for a next generation of secure data storage. Current magnetic memories, ranging from low-density stripes up to high-density hard drives, can ultimately be detected…
Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, efficient energy usage, and high storage capacity. A core concept…
Over the past decades, the relentless scaling and mass production of flash memory have underpinned the data-centric era. Yet charge-trap-based 3D NAND flash is now constrained by intrinsic physical and architectural limits, including…
We show a novel magneto-resistive effect that appears in lithographically shaped, three-arm nanostructure, fabricated from ferromagnetic (Ga,Mn)As layers. The effect, related to a rearrangement of magnetic domain walls between different…
We show that, in tantalum oxide resistive memories, activation power provides a multi-level variable for information storage that can be set and read separately from the resistance. These two state variables (resistance and activation…
Resistive memory (RM) based neuromorphic systems can emulate synaptic plasticity and thus support continual learning, but they generally lack biologically inspired mechanisms for active forgetting, which are critical for meeting modern data…
Voltage-controlled resistive switching is demonstrated in various gap systems on SiO2 substrate. The nanosized gaps are made by different means using different materials including metal, semiconductor, and metallic nonmetal. The switching…
Mechanical memory and computing are gaining significant traction as means to augment traditional electronics for robust and energy efficient performance in extreme environments. However, progress has largely focused on bistable…
Strongly-interacting nanomagnetic arrays are crucial across an ever-growing suite of technologies. Spanning neuromorphic computing, control over superconducting vortices and reconfigurable magnonics, the utility and appeal of these arrays…
In this study, we proposed and demonstrated a self-rectifying property of silicon nitride (Si3N4)-based resistive random access memory device by employing p-type silicon (p-Si) as bottom electrode. The RRAM devices consisted of…