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We present a simple nanodevice that can operate in two modes: i) three-state memory and ii) reading device. The nanodevice is fabricated with an array of ordered triangular-shaped nanomagnets embedded in a superconducting thin film. The…

Superconductivity · Physics 2016-08-05 J. del Valle , A. Gomez , E. M. Gonzalez , M. R. Osorio , D. Granados , J. L. Vicent

Many key electronic technologies (e.g., large-scale computing, machine learning, and superconducting electronics) require new memories that are fast, reliable, energy-efficient, and of low-impedance at the same time, which has remained a…

Applied Physics · Physics 2020-01-15 Lijun Zhu , Lujun Zhu , Shengjie Shi , D. C. Ralph , R. A. Buhrman

Electric-field-induced resistance switching (RS) phenomena have been studied for over 60 years in metal/dielectrics/metal structures. In these experiments a wide range of dielectrics have been studied including binary transition metal…

Materials Science · Physics 2013-06-05 Shang Da-Shang , Sun Ji-Rong , Shen Bao-Gen , Wuttig Matthias

Thermodynamic-driven filament formation in redox-based resistive memory and the impact of thermal fluctuations on switching probability of emerging magnetic switches are probabilistic phenomena in nature, and thus, processes of binary…

Other Condensed Matter · Physics 2013-10-21 Omid Kavehei , Efstratios Skafidas

Electrical characteristics of a Co/TiO_x/Co resistive memory device, fabricated by two different methods are reported. In addition to crystalline TiO_2 layers fabricated via conventional atomic layer deposition (ALD), an alternative method…

Topologically protected edge state transport in quantum materials is dissipationless and features quantized Hall conductance, and shows great potential in highly fault-tolerant computing technologies. However, it remains elusive about how…

This study investigates strategies for minimizing Joule losses in resistive random access memory (ReRAM) cells, which are also referred to as memristive devices. Typically, the structure of ReRAM cells involves a nanoscale layer of…

Emerging Technologies · Computer Science 2025-07-25 Valeriy A. Slipko , Yuriy V. Pershin

This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled)…

Applied Physics · Physics 2021-12-08 Javier del Valle , Juan Gabriel Ramírez , Marcelo J. Rozenberg , Ivan K. Schuller

The inherent stochasticity in many nano-scale devices makes them prospective candidates for low-power computations. Such devices have been demonstrated to exhibit probabilistic switching between two stable states to achieve stochastic…

Emerging Technologies · Computer Science 2018-05-24 Indranil Chakraborty , Amogh Agrawal , Kaushik Roy

Neuromorphic computing circuits can be realized using memristors based on low-dimensional materials enabling enhanced metal diffusion for resistive switching. Here, we investigate memristive properties of vertically aligned MoS$_2$…

Resistive memory based on 2D WS2, MoS2, and h-BN materials has been studied, including experiments and simulations. The influences with different active layer thicknesses have been discussed, including experiments and simulations. The…

Materials Science · Physics 2023-10-03 Ying-Chuan Chen , Yu-Ting Chao , Edward Chen , Chao-Hsin Wu , Yuh-Renn Wu

Volatile threshold resistive switching and neuronal oscillations in phase-change materials, specifically those undergoing metal-to-insulator transitions, offer unique attributes such as fast and low-field volatile switching, tunability, and…

Materials Science · Physics 2025-07-08 Huandong Chen , Jayakanth Ravichandran

Bipolar resistive switching using organic molecule is very promising for memory application owing to their advantages like simple device structure, low manufacturing cost, their stability and flexibility etc. Herein we report…

Memristive devices have drawn considerable research attention due to their potential applications in non-volatile memory and neuromorphic computing. The combination of resistive switching devices with light-responsive materials is…

Applied Physics · Physics 2020-09-22 Kamalakannan Ranganathan , Mor Feingenbaum , Ariel Ismach

The power and energy consumption of resistive switching devices can be lowered by reducing their active layer dimensions. Efforts to push this low-energy switching property to its limits have led to the investigation of active regions made…

Materials Science · Physics 2025-11-27 M. Kaniselvan , Y. R. Jeon , M. Mladenović , M. Luisier , D. Akinwande

Flexible electronics and neuromorphic computing face key challenges in material integration and function retention. In particular, freestanding membranes suffer from slow sacrificial layer removal and interfacial strain, while neuromorphic…

Large-scale integration of emerging nanoscale non-volatile memory devices, e.g. resistive random-access memory (RRAM), can enable a new generation of neuromorphic computers that can solve a wide range of machine learning problems. Such…

Emerging Technologies · Computer Science 2016-12-20 Xinyu Wu , Vishal Saxena

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…

Materials Science · Physics 2007-05-23 Rene Meyer , Hermann Kohlstedt

Non-volatile magnetic storage, from 1940s magnetic core to present day racetrack memory and magnetic anisotropy switching devices rely on the metastability of magnetic domains to store information. However, the inherent inefficiency of…

Memristors are passive circuit elements which behave as resistors with memory. The recent experimental realization of a memristor has triggered interest in this concept and its possible applications. Here, we demonstrate memristive response…

Strongly Correlated Electrons · Physics 2015-05-13 Tom Driscoll , Hyun-Tak Kim , Byung-Gyu Chae , Massimiliano Di Ventra , D. N. Basov
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