Related papers: Superconducting bimodal ionic photo-memristor
Neuromorphic computing aspires to overcome the intrinsic inefficiencies of von Neumann architectures by co-locating memory and computation in physical devices that emulate biological neurons and synapses. Memristive materials stand at the…
The transition to smart wearable and flexible optoelectronic devices communicating with each other and performing neuromorphic computing at the edge is a big goal in next-generation optoelectronics. These devices should perform their…
The electronic properties of interfaces between two different solids can differ strikingly from those of the constituent materials. For instance, metallic conductivity, and even superconductivity, have been recently discovered at interfaces…
The morphology and dimension of the conductive filament formed in a memristive device are strongly influenced by the thickness of its switching medium layer. Aggressive scaling of this active layer thickness is critical towards reducing the…
Memristors that mimic brain functions are crucial for energy-efficient neuromorphic devices. Ion channels that emulate biological synapses are still in the early stages of development, especially the tunability of memory states. Here, we…
Comprehensive understanding of the world's most energy efficient powerful computer, the human brain, is an elusive scientific issue. Still, already gained knowledge indicates memristors can be used as a building block to model the brain. At…
Neuromorphic hardware facilitates rapid and energy-efficient training and operation of neural network models for artificial intelligence. However, existing analog in-memory computing devices, like memristors, continue to face significant…
The potential of memristive devices is often seeing in implementing neuromorphic architectures for achieving brain-like computation. However, the designing procedures do not allow for extended manipulation of the material, unlike CMOS…
Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography, etc. Their fabrication control and…
Optical memristors represent a monumental leap in the fusion of photonics and electronics, heralding a new era of applications from neuromorphic computing to artificial intelligence. However, current technologies are hindered by complex…
We experimentally demonstrate a proof-of-principle implementation of an almost ideal memristor - a two-terminal circuit element whose resistance is approximately proportional to the integral of the input signal over time. The demonstrated…
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…
We propose a simple model of a nanoswitch as a memory resistor. The resistance of the nanoswitch is determined by electron tunneling through a nanoparticle diffusing around one or more potential minima located between the electrodes in the…
Physical systems exhibiting neuromechanical functions promise to enable structures with directly encoded autonomy and intelligence. We report on a class of neuromorphic metamaterials embodying bioinspired mechanosensing, memory, and…
The memristor is the fundamental non-linear circuit element, with uses in computing and computer memory. ReRAM (Resistive Random Access Memory) is a resistive switching memory proposed as a non-volatile memory. In this review we shall…
While two-terminal HfOX (x<2) memristor devices have been studied for ion transport and current evolution, there have been limited reports on the effect of the long-range thermal environment on their performance. In this work,…
Using memristive properties common for the titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to 7-bit precision)…
Modern computers perform pre-defined operations using static memory components, whereas biological systems learn through inherently dynamic, time-dependent processes in synapses and neurons. The biological learning process also relies on…
We propose a tunneling heterostructure by replacing one of the metal electrodes in a metal/ferroelectric/metal ferroelectric tunnel junction with a heavily doped semiconductor. In this metal/ferroelectric/semiconductor tunnel diode, both…
Fine-tuned ion transport across nanoscale pores is key to many biological processes such as neurotransmission. Recent advances have enabled the confinement of water and ions to two dimensions, unveiling transport properties unreachable at…