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In many cases, the behavior of physical memristive devices can be relatively well captured by using a single internal state variable. This study investigates the low-power control of first-order memristive devices to derive the most…

Emerging Technologies · Computer Science 2026-01-14 Valeriy A. Slipko , Alon Ascoli , Fernando Corinto , Yuriy V. Pershin

Redox-based nanoionic resistive memory cells (ReRAMs) are one of the most promising emerging nano-devices for future information technology with applications for memory, logic and neuromorphic computing. Recently, the serendipitous…

We present a multiscale simulation framework to compute the current vs. voltage (I-V ) characteristics of metal/oxide/metal structures building the core of conductive bridging random access memory (CBRAM) cells and to shed light on their…

Under certain conditions, applying a sequence of voltage pulses of alternating polarities across a resistive switching memory device induces a finite number of fixed-point attractors in its time-averaged dynamics, known as dynamical…

Mesoscale and Nanoscale Physics · Physics 2026-01-14 Valeriy A. Slipko , Alon Ascoli , Fernando Corinto , Yuriy V. Pershin

Redox-based resistive switching devices (ReRAM) are an emerging class of non-volatile storage elements suited for nanoscale memory applications. In terms of logic operations, ReRAM devices were suggested to be used as programmable…

Emerging Technologies · Computer Science 2015-03-02 A. Siemon , S. Menzel , R. Waser , E. Linn

Resistance switching memory cells such as electrochemical metallization cells and valence change mechanism cells have the potential to revolutionize information processing and storage. However, the creation of deterministic resistance…

Mesoscale and Nanoscale Physics · Physics 2023-10-03 V. A. Slipko , Y. V. Pershin

Resistance-change random access memory (RRAM) devices are nanoscale metal-insulator-metal structures that can store information in their resistance states, namely the high resistance (HRS) and low resistance (LRS) states. They are a…

Emerging Technologies · Computer Science 2022-06-14 Yuvraj Misra , Tarun Kumar Agarwal

Resistance switching devices are of special importance because of their application in resistive memories (RRAM) which are promising candidates for replacing current nonvolatile memories and realize storage class memories. These devices…

As conventional memory technologies are challenged by their technological physical limits, emerging technologies driven by novel materials are becoming an attractive option for future memory architectures. Among these technologies,…

Materials Science · Physics 2015-05-28 Omid Kavehei , Said Al-Sarawi , Kyoung-Rok Cho , Kamran Eshraghian , Derek Abbott

Nanometallic devices based on amorphous insulator-metal thin films are developed to provide a novel non-volatile resistance-switching random-access memory (RRAM). In these devices, data recording is controlled by a bipolar voltage, which…

Materials Science · Physics 2014-12-08 Xiang Yang

Memristive switching serves as the basis for a new generation of electronic devices. Memristors are two-terminal devices in which the current is turned on and off by redistributing point defects, e.g., vacancies, which is difficult to…

Emerging non-volatile memories (NVMs) have currently attracted great interest for their potential applications in advanced low-power information storage and processing technologies. Conventional NVMs, such as magnetic random access memory…

Solid-state programmable metallization cells have attracted considerable attention as memristive elements for Redox-based Resistive Random Access Memory (ReRAM) for low-power and low-voltage applications. In principle, liquid-state…

Chemical Physics · Physics 2016-08-26 Ji-Hyung Han , Ramachandran Muralidhar , Rainer Waser , Martin Z. Bazant

We show that ideal memristors - devices whose resistance is proportional to the charge that flows through them - can be realized using spin torque-driven viscous magnetization dynamics. The latter can be accomplished in the spin liquid…

Applied Physics · Physics 2020-06-16 Guanxiong Chen , Sergei Ivanov , Sergei Urazhdin

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…

Mesoscale and Nanoscale Physics · Physics 2013-04-17 Sergey E. Savel'ev , Fabio Marchesoni , Alexander M. Bratkovsky

Resistance switching random access memory (ReRAM), with the ability to repeatedly modulate electrical resistance, has been highlighted as a feasible high-density memory with the potential to replace negative-AND (NAND) flash memory. Such…

Mesoscale and Nanoscale Physics · Physics 2018-04-11 Yang Lu , Jung Ho Yoon , Yanhao Dong , I-Wei Chen

Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems. However a major challenge is to address the potentially…

Other Condensed Matter · Physics 2013-07-04 Siddharth Gaba , Patrick Sheridan , Jiantao Zhou , Shinhyun Choi , Wei Lu

We study a switching synchronization phenomenon taking place in one-dimensional memristive networks when the memristors switch from the high to low resistance state. It is assumed that the distributions of threshold voltages and switching…

Adaptation and Self-Organizing Systems · Physics 2017-12-27 V. A. Slipko , Y. V. Pershin

Much effort has been devoted to device and materials engineering to realize nanoscale resistance random access memory (RRAM) for practical applications, but there still lacks a rational physical basis to be relied on to design scalable…

Mesoscale and Nanoscale Physics · Physics 2017-04-12 Yang Lu , Jong Ho Lee , I-Wei Chen

Analog in-memory computing (AIMC) accelerators enable efficient deep neural network computation directly within memory using resistive crossbar arrays, where model parameters are represented by the conductance states of memristive devices.…

Machine Learning · Computer Science 2025-10-06 Jindan Li , Zhaoxian Wu , Gaowen Liu , Tayfun Gokmen , Tianyi Chen
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