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Memristor

About: Memristor is a research topic. Over the lifetime, 6014 publications have been published within this topic receiving 134936 citations.


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Journal ArticleDOI
29 Jan 2020-Nature
TL;DR: The fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs and an effective hybrid-training method to adapt to device imperfections and improve the overall system performance are proposed.
Abstract: Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks1–4. However, convolutional neural networks (CNNs)—one of the most important models for image recognition5—have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices6–9. Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST10 image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing. A fully hardware-based memristor convolutional neural network using a hybrid training method achieves an energy efficiency more than two orders of magnitude greater than that of graphics-processing units.

1,033 citations

Journal Article
TL;DR: It is shown that the hitherto published approaches to the modeling of boundary conditions need not conform with the requirements for the behavior of a practical circuit element, and the described SPICE model of the memristor is constructed as an open model, enabling additional modifications of non-linear boundary conditions.
Abstract: A mathematical model of the prototype of memristor, manufactured in 2008 in Hewlett-Packard Labs, is described in the paper. It is shown that the hitherto published approaches to the modeling of boundary conditions need not conform with the requirements for the behavior of a practical circuit element. The described SPICE model of the memristor is thus constructed as an open model, enabling additional modifications of non-linear boundary conditions. Its functionality is illustrated on computer simulations.

1,025 citations

Journal ArticleDOI
18 Sep 2009
TL;DR: It is argued that capacitive and inductive elements, namely, capacitors and inductors whose properties depend on the state and history of the system, are common at the nanoscale, where the dynamical properties of electrons and ions are likely to depend upon the history ofThe system, at least within certain time scales.
Abstract: We extend the notion of memristive systems to capacitive and inductive elements, namely, capacitors and inductors whose properties depend on the state and history of the system All these elements typically show pinched hysteretic loops in the two constitutive variables that define them: current-voltage for the memristor, charge-voltage for the memcapacitor, and current-flux for the meminductor We argue that these devices are common at the nanoscale, where the dynamical properties of electrons and ions are likely to depend on the history of the system, at least within certain time scales These elements and their combination in circuits open up new functionalities in electronics and are likely to find applications in neuromorphic devices to simulate learning, adaptive, and spontaneous behavior

913 citations

Journal ArticleDOI
TL;DR: It is demonstrated that voltage-controlled domain configurations in ferroelectric tunnel barriers yield memristive behaviour with resistance variations exceeding two orders of magnitude and a 10 ns operation speed.
Abstract: Memristors are devices whose dynamic properties are of interest because they can mimic the operation of biological synapses. The demonstration that ferroelectric domains in tunnel junctions behave like memristors suggests new approaches for designing neuromorphic circuits.

906 citations

Journal ArticleDOI
TL;DR: A high-density, fully operational hybrid crossbar/CMOS system composed of a transistor- and diode-less memristor crossbar array vertically integrated on top of a CMOS chip by taking advantage of the intrinsic nonlinear characteristics of the Memristor element.
Abstract: Crossbar arrays based on two-terminal resistive switches have been proposed as a leading candidate for future memory and logic applications. Here we demonstrate a high-density, fully operational hybrid crossbar/CMOS system composed of a transistor- and diode-less memristor crossbar array vertically integrated on top of a CMOS chip by taking advantage of the intrinsic nonlinear characteristics of the memristor element. The hybrid crossbar/CMOS system can reliably store complex binary and multilevel 1600 pixel bitmap images using a new programming scheme.

853 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023768
20221,599
2021713
2020694
2019765