Analog Memristors Based on Thickening/Thinning of Ag Nanofilaments in Amorphous Manganite Thin Films
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We developed an analog memristor based on the thickening/thinning of Ag nanofilaments in amorphous La(1-x)Sr(x)MnO3 (a-LSMO) thin films. The Ag/a-LSMO/Pt memristor exhibited excellent pinched hysteresis loops under high-excitation frequency, and the areas enclosed by the pinched hysteresis loops shrank with increasing excitation frequency, which is a characteristic typical of a memristor. The memristor also showed continuously tunable synapselike resistance and stable endurance. The a-LSMO thin films in the memristor acted as a solid electrolyte for Ag(+) cations, and only the Ag/a-LSMO/Pt memristor electroformed with a larger current compliance easily exhibited high-frequency pinched hysteresis loops. On the basis of the electrochemical metallization (ECM) theory and electrical transport models of quantum wires and nanowires, we concluded that the memristance is ultimately determined by the amount of charge supplied by the external current. The state equations of the memristor were established, and charge was the state variable. This study provides a new analog memristor based on metal nanofilaments thickening/thinning in ECM cells, which can be extended to other resistive switching materials. The new memristor may enable the development of beyond von Neumann computers.Keywords:
Memristor
Hysteresis
Electroforming
Manganite
Resistive touchscreen
Memistor
We have studied the effect of top electrode materials on the switching behavior of TaOx based memristors with an identical switching oxide layer and bottom electrode stack. We found that the virgin resistance, electroforming and switching performance depend heavily on the chemical property of the top electrode materials. In addition, the electrical properties of metal oxides formed with the top electrodes also contribute to the overall memristor performance, including the nonlinearity of the current–voltage relationship. These results provide insights into understanding of memristor behavior as well as approaches for device property engineering.
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Electroforming
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Nanoscale memristors open up new opportunities for the development of brain neural networks. Simple and precise memristors enhance the performance of various neural networks and operational circuits. In this letter, a three-terminal memristor is proposed, which makes the memristor more flexible and practical in circuit design and application through the introduction of a control port. Consid-ering that the resistance of a three-terminal memristor consists of three parts, i.e., metal region, low-resistance region, and high-resistance region, a three-segment piecewiselinear method is applied to fit these three regions. The model of this memristor is constructed through the derivation of the memristor formula and working principle.Candence simulations are conducted on the resultant circuit to verify its correctness.
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Memistor
Port (circuit theory)
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The memristor, which is the fourth passive element that is lacking, is a nonvolatile device with two terminals. It promises advancement in future technology, which will aid in the reduction of power consumption, the reduction of cost, and the increase of performance of integrated circuits. This work presents a thorough investigation of memristor modeling through the use of Mat lab simulations. For the purpose of anticipating the behavior of the memristor device, we consider three different modeling strategies. In addition to the solid-state thin film memristor device, a spintronic memristor device based on magnetic technology was also simulated in this study. The fact that it has nanoscale geometry means that it is susceptible to process fluctuations during the fabrication process. The electrical behavior of the memristor deviates from the desired values as a result of process changes. As a result, the yield of a memristor-based memory design is lowered as a result. Also discussed in this study is a concrete model of a spintronic device that is based on the mechanism of magnetic-domain-wall motion and is described in detail.
Memristor
Memistor
Non-Volatile Memory
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Memristor
Memistor
Neuromorphic engineering
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The memristance variation of a single memristor with voltage input is generally a nonlinear function of time. Linearization of memristance variation about time is very important for the easiness of memristor programming. In this paper, a method utilizing an anti-serial architecture for linear programming is addressed. The anti-serial architecture is composed of two memristors with opposite polarities. It linearizes the variation of memristance by virtue of complimentary actions of two memristors. For programming a memristor, additional memristor with opposite polarity is employed. The linearization effect of weight programming of an anti-serial architecture is investigated and memristor bridge synapse which is built with two sets of anti-serial memristor architecture is taken as an application example of the proposed method.
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Memistor
Linearization
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Memristor had been first theorized nearly 40 years ago by Prof. Chua, as the fourth fundamental circuit element beside the three existing elements (Resistor, Capacitor and Inductor) but because no one has succeeded in building a memristor, it has long remained a theoretical element. Some months ago, Hewlett-Packard (hp) announced it created a memristor using a TiO 2 /TiO 2-X structure. In this paper, the characteristics, structures and relations for the invented hp's memristor are briefly reviewed and then two general SPICE models for the charge-controlled and flux-controlled memristors are introduced for the first time. By adjusting the model parameters to the hp's memristor characteristics some circuit properties of the device are studied and then two important memristor applications as the memory cell in a nonvolatile-RAM structure and as the synapse in an artificial neural network are studied. By utilizing the introduced models and designing the appropriate circuits for two most important applications; a nonvolatile memory structure and a programmable logic gate, circuit simulations are done and the results are presented.
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Memistor
Spice
Electrical element
Non-Volatile Memory
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Memristor crossbar arrays were fabricated based on a Ti/HfO2/Ti stack that exhibited electroforming-free behavior and low device variability in a 10 x 10 array size. The binary states of high-resistance-state and low-resistance-state in the bipolar memristor device were used for the synaptic weight representation of a binarized neural network. The electroforming-free memristor was confirmed as being suitable as a binary synaptic device because of its higher device yield, lower variability, and less severe malfunction (for example, hard break-down) than the electroformed memristors based on a Ti/HfO2/Pt structure. The feasibly working binarized neural network adopting the electroforming-free binary memristors was demonstrated through simulation.
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Electroforming
Memistor
Neuromorphic engineering
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The advent of the memristor breaks the scaling limitations of MOS technology and prevails over emerging semiconductor devices. In this paper, various memristor models including behaviour, spice, and experimental are investigated and compared with the memristor's characteristic equations and fingerprints. It has brought to light that most memristor models need a window function to resolve boundary conditions. Various challenges of availed window functions are discussed with matlab's simulated results. Biolek's window is a most acceptable window function for the memristor, since it limits boundaries growth as well as sticking of states at boundaries. Simmons tunnel model of a memristor is the most accepted model of a memristor till now. The memristor is exploited very frequently in memory designing and became a prominent candidate for futuristic memories. Here, several memory structures utilizing the memristor are discussed. It is seen that a memristor-transistor hybrid memory cell has fast read/write and low power operations. Whereas, a 1T1R structure provides very simple, nanoscale, and non-volatile memory that has capabilities to replace conventional Flash memories. Moreover, the memristor is frequently used in SRAM cell structures to make them have non-volatile memory. This paper contributes various aspects and recent developments in memristor based circuits, which can enhance the ongoing requirements of modern designing criterion.
Memristor
Memistor
Non-volatile random-access memory
Non-Volatile Memory
Flash Memory
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The present study considers the reviews done on memristor in the recent years, and also investigates three different models of memristor structure including linear, non-linear and the performance of memristor in high-chaos circuits. As the results of the simulation indicate non-linear model has a better performance than linear one. Two distinct features of memristor include low power consumption and its capability to have a memory.
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Memistor
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Memristors are passive non-linear circuit components with memory characteristics, and have been recognized as the fourth basic circuit component, along with resistors, capacitors, and inductors. It has been nearly half a century since the conceptualisation of the memristor, and related research has mainly focussed on the two aspects of binary and continuous memristors. However, compared with these two types of memristors, tri-state and multi-state memristors have greater data density per device, with rich dynamics and great potential in logic and chaotic circuit applications. Moreover, previous studies show that the series-parallel connection of memristor generates more diverse circuit behaviours and increased capacity over a single memristor. However, most of this research is based on mathematical analysis, and lack behavioural circuit simulations or experimental validation. Here, the tri-state memristor is proposed and the mathematic and equivalent Spice models of the tri-state memristor is shown. Furthermore, the circuit characteristics are studied with a complete characterisation of its series-parallel behaviours of the tri-state memristor. Simulations are performed with LTSpice, and the results verify the theoretical analysis, which provides a strong experimental basis for the study of combinational memristive circuits.
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Memistor
Spice
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