相关论文: Memristive Systems Analysis of 3-Terminal Devices
The advent of memristors and resistive switching has transformed solid state physics, enabling advanced applications such as neuromorphic computing. Inspired by these developments, we introduce the concept of Mem-emitters, devices that…
The advent of deep learning has resulted in a number of applications which have transformed the landscape of the research area in which it has been applied. However, with an increase in popularity, the complexity of classical deep neural…
Human brain processes sensory information in real-time with extraordinary efficiency compared to the possibilities of current artificial computing systems. It operates as a complex nonlinear system, composed of interacting dynamic units -…
Resistive switching devices emerged a huge amount of interest as promising candidates for non-volatile memories as well as artificial synapses due to their memristive behavior. The main physical and chemical phenomena which define their…
Cuprate superconductors are strongly sensitive materials to disorder and oxygen stoichiometry; even minute variations of those parameters drastically change their electronic properties. Here we exploit this characteristic to engineer a…
Reversible bipolar nano-switches that can be set and read electronically in a solid-state two-terminal device are very promising for applications. We have performed molecular-dynamics simulations that mimic systems with oxygen vacancies…
A memristive device is a novel passive device, which is essentially a resistor with memory. This device can be utilized for novel technical applications like neuromorphic computation. In this paper, we focus on anticipation - a capability…
The current work proposes a novel scheme for developing a light-activated non-filamentary memristor device by fabricating an Au-nanoparticle embedded HfO$_2$-bilayer/p-Si MOS structure. Under illumination, the electrons in such embedded…
Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, specially for edge computing. Recent progress in…
Memristive materials are related to neuromorphic applications as they can combine information processing with memory storage in a single computational element, just as biological neurons. Many of these bioinspired materials emulate the…
Reasoned by its dynamical behavior, the memristor enables a lot of new applications in analog circuit design. Since some realizations are shown (e.g. 2007 by Hewlett Packard), the development of applications with memristors becomes more and…
Memristors are non-volatile nano-resistors. Their resistance can be tuned by applied currents or voltages and set to a large number of levels between two limit values. Thanks to these properties, memristors are ideal building blocks for a…
A large number of simulation models have been proposed over the years to mimic the electrical behaviour of memristive devices. The models are based either on sophisticated mathematical formulations that do not account for physical and…
This paper examines the coexistence of resistive, capacitive, and inertia (virtual inductive) effects in memristive devices, focusing on ReRAM devices, specifically the interface-type or non-filamentary analog switching devices. A…
We discuss the physical properties of realistic memristive, memcapacitive and meminductive systems. In particular, by employing the well-known theory of response functions and microscopic derivations, we show that resistors, capacitors and…
The possibility to develop neuromorphic computing devices able to mimic the extraordinary data processing capabilities of biological systems spurs the research on memristive systems. Memristors with additional functionalities such as robust…
In the present work we describe a way to assess memory capability of real devices, while proposing to the engineering community what to pursue to create devices with deep associated memory capability. The study of the signal produced by a…
Memristors are promising next-generation memory candidates that are nonvolatile, possess low power requirements and are capable of nanoscale fabrication. In this article we physically realise and describe the use of organic memristors in…
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…
Memristors offer significant advantages as in-memory computing devices due to their non-volatility, low power consumption, and history-dependent conductivity. These attributes are particularly valuable in the realm of neuromorphic circuits…