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Showing papers on "Memistor published in 2018"


Journal ArticleDOI
TL;DR: A novel circuit for Memristor-based multilayer neural networks is presented, which can use a single memristor array to realize both the plus and minus weight of the neural synapses.
Abstract: Memristors are promising components for applications in nonvolatile memory, logic circuits, and neuromorphic computing. In this paper, a novel circuit for memristor-based multilayer neural networks is presented, which can use a single memristor array to realize both the plus and minus weight of the neural synapses. In addition, memristor-based switches are utilized during the learning process to update the weight of the memristor-based synapses. Moreover, an adaptive back propagation algorithm suitable for the proposed memristor-based multilayer neural network is applied to train the neural networks and perform the XOR function and character recognition. Another highlight of this paper is that the robustness of the proposed memristor-based multilayer neural network exhibits higher recognition rates and fewer cycles as compared with other multilayer neural networks.

163 citations


Journal ArticleDOI
TL;DR: A simulation platform for the memristor-based neuromorphic system, called MNSIM, which can optimize the design and estimate the tradeoff relationships among different performance metrics for users and achieves over 7000 times speed-up than SPICE simulation.
Abstract: Memristor-based computation provides a promising solution to boost the power efficiency of the neuromorphic computing system. However, a behavior-level memristor-based neuromorphic computing simulator, which can model the performance and realize an early stage design space exploration, is still missing. In this paper, we propose a simulation platform for the memristor-based neuromorphic system, called MNSIM. A hierarchical structure for memristor-based neuromorphic computing accelerator is proposed to provides flexible interfaces for customization. A detailed reference design is provided for large-scale applications. A behavior-level computing accuracy model is incorporated to evaluate the computing error rate affected by interconnect lines and nonideal device factors. Experimental results show that MNSIM achieves over 7000 times speed-up than SPICE simulation. MNSIM can optimize the design and estimate the tradeoff relationships among different performance metrics for users.

80 citations


Journal ArticleDOI
TL;DR: This brief proposes a novel locally active memristor based on a voltage-controlled genericmemristor model and uses the analysis methods of standard nonlinear theory to analyze its characteristics and illustrates the concept of local activity via the dc v-i loci of memristors and non-volatile memory via the power-off plot of mem Bristor.
Abstract: In this brief, we propose a novel locally active memristor based on a voltage-controlled generic memristor model and use the analysis methods of standard nonlinear theory to analyze its characteristics and illustrate the concept of local activity via the dc v-i loci of memristor and non-volatile memory via the power-off plot of memristor. A chaotic attractor is observed with a simple nonlinear circuit that only includes three circuit elements in parallel: 1) a nonlinear locally active memristor; 2) a linear passive inductor; and 3) a linear passive capacitor. Then, we analyze the dynamical characteristics of the above circuit and show complex bifurcation behaviors.

66 citations


Journal ArticleDOI
TL;DR: This model is based on experimental measurements of PCM devices fabricated in the 90 nm technology node and exploits memristor theory to obtain a simple and reliable circuit model based on electrical variables such as charge and flux.
Abstract: Phase-change memory (PCM) is one of the most promising non-volatile memory technologies and is finding applications in areas such as storage-class memory and emerging non-von Neumann computing systems. Even though powerful physics-based models have been developed for these devices, there is a lack of simple and accurate circuit models to describe these elements. In this brief, we exploit memristor theory to obtain a simple and reliable circuit model based on electrical variables such as charge and flux . This model is based on experimental measurements of PCM devices fabricated in the 90 nm technology node.

32 citations


Journal ArticleDOI
TL;DR: A mapping methodology of large Boolean logic circuits on memristor crossbar, as well as several optimization schemes are proposed to efficiently map the circuits on the crossbar.
Abstract: Alternatives to CMOS logic circuit implementations are under research for future scaled electronics. Memristor crossbar-based logic circuit is one of the promising candidates to at least partially replace CMOS technology, which is facing many challenges such as reduced scalability, reliability, and performance gain. Memristor crossbar offers many advantages including scalability, high integration density, nonvolatility, etc. The state-of-the-art for memristor crossbar logic circuit design can only implement simple and small circuits. This paper proposes a mapping methodology of large Boolean logic circuits on memristor crossbar. Appropriate place-and-route schemes, to efficiently map the circuits on the crossbar, as well as several optimization schemes are also proposed. To illustrate the potential of the methodology, a multibit adder and other nine more complex benchmarks are studied; the delay, area and power consumption induced by both crossbar and its CMOS control part are evaluated.

30 citations


Journal ArticleDOI
TL;DR: A memristor-based neuromorphic system that can be used for ex situ training of various multi-layer neural network algorithms, based on an analogue neuron circuit that is capable of performing an accurate dot product calculation is described.
Abstract: This paper describes a memristor-based neuromorphic system that can be used for ex situ training of various multi-layer neural network algorithms. This system is based on an analogue neuron circuit that is capable of performing an accurate dot product calculation. The presented ex situ programming technique can be used to map many key neural algorithms directly onto the grid of resistances in a memristor crossbar. Using this weight-to-crossbar mapping approach along with the memristor based circuit architecture, complex neural algorithms can be easily implemented using this system. Some existing memristor based circuits provide an approximated dot product based on conductance summation, but neuron outputs are not directly correlated to the numerical values obtained in a traditional software approach. To show the effectiveness and versatility of this circuit, two different powerful neural networks were simulated. These include a Restricted Boltzmann Machine for character recognition and a Multilaye...

25 citations


Journal ArticleDOI
TL;DR: This paper presents a compact and efficient scheme of reading and writing for two memristors per transistor-based multivalued memory using the VTEAM model of the memristor and verification of feasibility of reading operations and writing operations for multivaluing memory is achieved through HSPICE simulation.
Abstract: The multivalued memory achieved with memristors is a promising approach to enhance the memory density. Effective and compact methods of reading and writing for multivalued memories can significantly improve the performance of circuits. In this paper, we present a compact and efficient scheme of reading and writing for two memristors per transistor-based multivalued memory. With the VTEAM model of the memristor, the verification of feasibility of our reading operations and writing operations for multivalued memory is achieved through HSPICE simulation.

18 citations


Journal ArticleDOI
TL;DR: A novel memistor-based neuron circuit, in which the memristor-CMOS hybrid synaptic circuit simply utilizes the positive voltage to change the Memristor and output voltage changes in the neuron circuit is proposed.
Abstract: This paper proposes a novel memistor-based neuron circuit, in which the memristor-CMOS hybrid synaptic circuit simply utilizes the positive voltage to change the memristance. At the same time, in order to obtain the memristance and output voltage changes in the neuron circuit, a mathematical deduction is implemented according to the circuit structure. Then, the memristor-based recognition and recall network circuits are constructed based on the proposed neuron circuit. The recognition and recall functions are realized by associative learning between the unconditional stimuli and the conditional stimuli (CS). After the learning stages, the single presentation of CS activates forgetting stages in the two networks. Furthermore, the related parameter changes in the learning and forgetting stages can be calculated by the deduced equations approximately. The PSPICE simulations are implemented to demonstrate the effectiveness of the proposed circuits and the deduced equations.

18 citations


Journal ArticleDOI
TL;DR: It is shown that a range of resistance values can be set within these memristor devices using a pulse train for programming, which demonstrates that lithium niobate memristors are strong candidates for use in neuromorphic computing.
Abstract: Memristor crossbars are capable of implementing learning algorithms in a much more energy and area efficient manner compared to traditional systems. However, the programmable nature of memristor crossbars must first be explored on a smaller scale to see which memristor device structures are most suitable for applications in reconfigurable computing. In this paper, we demonstrate the programmability of memristor devices with filamentary switching based on LiNbO3, a new resistive switching oxide. We show that a range of resistance values can be set within these memristor devices using a pulse train for programming. We also show that a neuromorphic crossbar containing eight memristors was capable of correctly implementing an OR function. This work demonstrates that lithium niobate memristors are strong candidates for use in neuromorphic computing.

18 citations


Journal ArticleDOI
TL;DR: It is proposed that memristors can potentially be used as switches for designing a reconfigurable dual-band RF/microwave planar filter, designed for multi-band receiver application using only one memristor-based switch.
Abstract: Summary Memristor-based technology could be utilized to enhance the performance of many radio frequency (RF)/microwave subsystems, such as filters. In this paper, we propose that memristors can potentially be used as switches for designing a reconfigurable dual-band RF/microwave planar filter. We are motivated to use memristors instead of some traditional microwave components because memristors do not require any bias, and no moving parts are involved. The reconfigurable filter is designed for multi-band receiver application using only one memristor-based switch. Circuit-level simulations are used to investigate memristor-based RF/microwave circuits and study their performance. The memristive RF switch is modeled by a resistor in the ON state and by a capacitor in the OFF state. An RF/microwave circuit simulator, NI AWR Microwave Office, is used to verify the expected functionality of the proposed memristor-based filter. Copyright © 2017 John Wiley & Sons, Ltd.

12 citations


Journal ArticleDOI
TL;DR: In this paper, the authors present a possible application of memristive switches for implementation of main-line mounted loaded-line phase shifters, where they replace PIN diodes, acting as RF/microwave switches, with memristors in order to reduce power consumption.

Journal ArticleDOI
01 Mar 2018-Optik
TL;DR: Results verified by Matlab and Multisim simulations indicate that the proposed memristive system produces a pinched hysteresis loop for a periodic input in the electric circuit.

Book ChapterDOI
01 Jan 2018
TL;DR: A fully symbolic model of the memristor is introduced that is used for the symbolic analysis of the amplifier configurations and a transmemristance amplifier is used as a case study of design when the nullor is substituted by a memistor.
Abstract: The memristor as an actual device was introduced in April 2008 at the HP labs, while its original foundations are dated from 1971 when Prof. L. O. Chua devised the memristor as the fourth basic circuit element. Nowadays, the memristor has captured most of the attention not only from circuit theoreticians, but also from circuit designers because the widely open possibilities of the device in applications where it co-exists with traditional electronics. A particular case of such an application arises when the memristor is combined with the nullor in order to achieve a memristive input-output transfer function. In this chapter, we firstly introduce a fully symbolic model of the memristor that is used for the symbolic analysis of the amplifier configurations. It is important to point out the symbolic nature of our memristor model in contrast with other models that are of numerical nature or implemented in a macro-equivalent. Secondly, the four single-loop negative-feedback nullor-based amplifier configurations are introduced, and their corresponding analytic transfer functions are generated and characterised. Similarly, the noise and harmonic distortion analyses are carried out on the four configurations yielding fully symbolic expressions for both, the output equivalent noise and the harmonic components. In a next step, the nullor is synthesised by using a memistor, which is a combination of two memristors connected back-to-back. Finally, a transmemristance amplifier is used as a case study of design when the nullor is substituted by a memistor. Along the manuscript, the resulting expressions from the mathematical analyses are verified with HSPICE simulations that incorporate the memristor model from a description in the VERILOG-A language.

Proceedings ArticleDOI
01 Oct 2018
TL;DR: In this article, the RF single-pole double-throw switch based on memistor is presented, which combines features of FET-based RF switches and already published memristor-based switches.
Abstract: In this paper, the RF single-pole double-throw switch based on memistor is presented. This idea combines features of FET-based RF switches and already published memristor-based switches. The circuit function does not require the continuous DC bias signal and therefore either auxiliary capacitors or inductors. The SPICE simulations are based on the model obtained from physical measurements reported in the literature.

Book ChapterDOI
01 Jan 2018
TL;DR: In this article, a physics-based mathematical model for anionic memristor devices is presented to simulate and predict the effect of oxide thickness, material type, and operating temperatures on the electrical characteristics of the device.
Abstract: This chapter presents a physics-based mathematical model for anionic memristor devices. The model utilizes Poisson Boltzmann equation to account for temperature effect on device potential at equilibrium and comprehends material effect on device behaviors. A detailed MATLAB-based algorithm is developed to clarify and simplify the simulation environment. Moreover, the provided model is used to simulate and predict the effect of oxide thickness, material type, and operating temperatures on the electrical characteristics of the device. The value of this contribution is to provide a framework intended to simulate anionic memristor devices using correlated mathematical models. In addition, the model can be used to explore device materials and predict its performance.