Related papers: A Compact Model of Interface-Type Memristors Linki…
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…
An ideal memristor is defined as a resistor with memory that, when subject to a time-dependent current, $I(t)$, its resistance $R_M(q)$ depends {\it only} on the charge $q$ that has flowed through it, so that its voltage response is…
Memristors, initially introduced in the 1970s, have received increased attention upon successful synthesis in 2008. Considerable work has been done on modeling and applications in specific areas, however, very little is known on the…
Aside from recent advances in artificial intelligence (AI) models, specialized AI hardware is crucial to address large volumes of unstructured and dynamic data. Hardware-based AI, built on conventional complementary metal-oxidesemiconductor…
Memristors, which are characterized by their unique input-voltage-history-dependent resistance, have garnered significant attention for the exploration of next-generation in-memory computing, reconfigurable logic circuits, and neural…
Usually, resistive switching (RS) devices show digital RS memory (sharp SET and RESET process), which is most suitable for digital data storage applications. Some RS devices also manifest ideal memristive behavior or analog memory…
Ferroelectric memristive devices based on hafnia are promising systems for neuromorphic electronics, yet the interplay between polarization-modulated resistive changes and defect-mediated transport often leads to complex and debated…
In this study, direct-current reactive sputtered ZnO and ZnO1-x based thin film (30 nm and 300 nm in thickness) memristor devices were produced and the effects of oxygen vacancies and thickness on the memristive characteristics were…
Recent advances in nanoscale science and technology provide possibilities to directly self-assemble and integrate functional circuit elements within the wiring scheme of devices with potentially unique architectures. Electroionic resistive…
To obtain precisely controllable, robust as well as reproduceable memristor for efficient neuromorphic computing still very challenging. Molecular tailoring aims at obtaining the much more flexibly tuning plasticity has recently generated…
Different real-world cognitive tasks evolve on different relevant timescales. Processing these tasks requires memory mechanisms able to match their specific time constants. In particular, the working memory utilizes mechanisms that span…
In this paper, the resistive switching and neuromorphic behavior of memristive devices based on parylene, a polymer both low-cost and safe for the human body, is comprehensively studied. The Metal/Parylene/ITO sandwich structures were…
Dynamic reconfiguration of charge carriers in confined ion-channels under electrical stimulation produces memory effects, where the internal resistance depends on history of the electric field. Vermiculite nanofluidic devices harness this…
We have calculated the key characteristics of associative (content-addressable) spatial-temporal memories based on neuromorphic networks with restricted connectivity - "CrossNets". Such networks may be naturally implemented in…
Since its inception the memristive fuse has been a good example of how small numbers of memristors can be combined to obtain useful behaviours unachievable by individual devices. In this work, we link the memristive fuse concept with that…
In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is…
Volatile memristors have recently gained popularity as promising devices for neuromorphic circuits, capable of mimicking the leaky function of neurons and offering advantages over capacitor-based circuits in terms of power dissipation and…
Nanofluidic memristors have demonstrated great potential for neuromorphic system applications with the advantages of low energy consumption and excellent biocompatibility. Here, an effective way is developed to regulate the memristive…
Superlattices may play an important role in next generation electronic and spintronic devices if the key-challenge of the reading and writing data can be solved. This challenge emerges from the coupling of low dimensional individual layers…
Reservoir computing is a machine learning paradigm that uses a high-dimensional dynamical system, or \emph{reservoir}, to approximate and predict time series data. The scale, speed and power usage of reservoir computers could be enhanced by…