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Memistor

About: Memistor is a research topic. Over the lifetime, 608 publications have been published within this topic receiving 34905 citations.


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Proceedings ArticleDOI
01 Dec 2014
TL;DR: The staircase memristor which has multiple stable resistive states is modelled and exploited, and its possible applications are investigated, and it shows a significant delayed-switching effect which yields a range of more or less discrete memristance values rather than continuous values.
Abstract: Memristor is a newly emerged device in nano-technology that provides low power consumption as well as high density. In this paper, the staircase memristor which has multiple stable resistive states is modelled and exploited, and its possible applications are investigated. Comparing with the analog memristor model proposed by HP, the staircase memristor model shows a significant delayed-switching effect which yields a range of more or less discrete memristance values rather than continuous values. We present using the staircase memristor to produce staircase waveforms which can be used in testing circuit or sampling oscilloscope. Besides, staircase memristors are used in cellular neural network (CNN) circuits to represent the connection weights between CNN cells. Benefit from the simple structure, non-volatility and low power properties, the complexity and size of CNN circuits can be reduced.

2 citations

Proceedings ArticleDOI
12 May 2016
TL;DR: It is shown that for a given memristor with a specified threshold current and Off resistance, there exists a trade-off between the size and the resolution of the circuit (i.e., the larger the size of the Circuit, the lower the resolution becomes).
Abstract: Neuromorphic circuits have recently emerged as promising candidates for future computing paradigms. Min-Max circuits are indispensable building blocks in many neuromorphic systems, fuzzy systems and artificial neural networks. An important challenge in the design of state-of-the-art min-max circuits is their area occupancy. Memristor-based min-max circuits have been sought as powerful candidates in the design of min-max circuits due to their miniaturized size. An important characteristic of the memristor is the existence of a Voltage/Current threshold. This threshold places constraints on the values of the input voltages to the circuit. More importantly, these constraints vary with the size (i.e., the number of inputs to the circuit) of the min-max circuit. In this work, the effect of the memristor threshold on memristor-based min-max circuits is modeled. It is shown that for a given memristor with a specified threshold current and Off resistance, there exists a trade-off between the size and the resolution of the circuit (i.e., the larger the size of the circuit, the lower the resolution becomes). All results are validated against Spice simulations by Eldo from Mentor Graphics using the TEAM model.

2 citations

Book ChapterDOI
01 Jan 2015
TL;DR: This work presents a circuit which responds with binary outputs to the signal exiting from the memristors implemented in an artificial neural system that functions through a high efficiency learning algorithm.
Abstract: Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks [1]. This kind of technology has permitted the implementation of a large number of real world data in an evolutionary learning artificial system. Human brain is capable of processing such data with standard always equal signals that are the synapsis. Our goal is to present a circuit which responds with binary outputs to the signal exiting from the memristors implemented in an artificial neural system that functions through a high efficiency learning algorithm.

2 citations

Proceedings ArticleDOI
24 Apr 2015
TL;DR: An accurate and flexible Threshold Adaptive Memristor (TEAM) model derived from the recognized classical Simmons Tunnel Model, which takes the ions diffusion by Joule Heat effects into consideration is addressed.
Abstract: The memristor, known as the fourth circuit element was theoretically predicted by Chua in 1971 and has been realized practically in 2008. However, with the wide applications of memristors, conventional memristor models were suffered from more challenges such as practicality, accuracy and flexibility. This paper addresses an accurate and flexible Threshold Adaptive Memristor (TEAM) model derived from the recognized classical Simmons Tunnel Model, which takes the ions diffusion by Joule Heat effects into consideration. Concretely, TEAM crossbar circuits are applied both in binary and gray scale image storage with the improved control method. The results reveal the approach polishes up the efficiency of parallel processing in nonvolatile memristive image process.

2 citations

Proceedings ArticleDOI
01 Aug 2013
TL;DR: This work first investigates the memristor-based synapse design and the corresponding training scheme, and a case study of an 8-bit arithmetic logic unit (ALU) design is used to demonstrate the hardware implementation of reconfigurable system built based on Memristor synapses.
Abstract: Scientists have dreamed of an information system with cognitive human-like skills for years. However, constrained by the device characteristics and rapidly increasing design complexity under the traditional processing technology, little progress has been made in hardware implementation. The recently popularized memristor offers a potential breakthrough for neuromorphic computing because of its unique properties including nonvolatily, extremely high fabrication density, and sensitivity to historic voltage/current behavior. In this work, we first investigate the memristor-based synapse design and the corresponding training scheme. Then, a case study of an 8-bit arithmetic logic unit (ALU) design is used to demonstrate the hardware implementation of reconfigurable system built based on memristor synapses.

2 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202328
202277
20212
20201
20191
201815