Memristor augmented ReRAM cell for cross-bar memory architecture
01 Mar 2017-pp 456-462
TL;DR: The main finding of this work is that, when this model is applied to a crossbar structure, there is no resistance change except the desired one because of its voltage control nature in comparison to other models.
Abstract: Memristor (the so called Resistive Random Access Memory (Re-RAM), is an emerging next generation non-volatile memory, which shows promise towards achieving faster operation speed and also various advantages such as non-volatility, low power consumption, most importantly lesser density and latency. It can store information and can also switch between different states. It is a two terminal device. This type of memories would not lose its data even when the power is switched off. Recently Memristor's applications lie even in complex and interesting areas like Artificial Intelligence. Memristor's can be used to model human brain since its properties is more similar to synapses. Therefore with the help of synapse as Memristor and neurons as a CMOS control circuit, the entire brain can be modeled and fabricated on a single chip. Memristor can replace the power consuming transistors which can be productive in creating a logic circuit. This allows flexibility in using a circuit both for storage purpose and logical operations simultaneously. Memories are usually designed based on the crossbar architecture, where a single switching cell (1Memristor in our case) is placed at the cross-points of word line and bit line. The main finding of this work is that, when this model is applied to a crossbar structure, there is no resistance change except the desired one because of its voltage control nature in comparison to other models. As a result we are avoiding the undesired current the so called sneak current along with reducing the circuit elements i.e. the technique involved in a complete arrest of sneak path current like complementary resistive switching or connecting a diode for each cell as in other models (Linear ion Drift model, TEAM model) for memory operation. The reduction in circuit elements has helped in enhancing the density, making the design less complex and reduction in die area.
Citations
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02 Oct 2022
TL;DR: In this paper , a comprehensive description and analysis of the memristor models relying upon their I-V curve is presented, including nonlinear, linear, VTEAM, and TEAM along with different window functions.
Abstract: This paper provides an overview of memristor methodology, starting from the definition and formulation of memristor progressing through implementation and contemporary literature. Numerous researchers have suggested several modelling methods as well as window functions to analyse the characteristics of memristor device. These models include nonlinear, linear, VTEAM, and TEAM along with different window functions. To handle the boundary issues, various window functions came into existence. From neuromorphic to memory systems, a memristor can be employed in a wide range of scenarios. In comparison with other devices, this device's distinctive features, such as better scalability, non-volatility, compatibility, and minimal feature size, made it more effective. This paper presents a comprehensive description and analysis of the memristor models relying upon their I-V curve.
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TL;DR: It is shown, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage.
Abstract: Anyone who ever took an electronics laboratory class will be familiar with the fundamental passive circuit elements: the resistor, the capacitor and the inductor. However, in 1971 Leon Chua reasoned from symmetry arguments that there should be a fourth fundamental element, which he called a memristor (short for memory resistor). Although he showed that such an element has many interesting and valuable circuit properties, until now no one has presented either a useful physical model or an example of a memristor. Here we show, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage. These results serve as the foundation for understanding a wide range of hysteretic current-voltage behaviour observed in many nanoscale electronic devices that involve the motion of charged atomic or molecular species, in particular certain titanium dioxide cross-point switches.
8,971 citations
TL;DR: In this article, the memristor is introduced as the fourth basic circuit element and an electromagnetic field interpretation of this relationship in terms of a quasi-static expansion of Maxwell's equations is presented.
Abstract: A new two-terminal circuit element-called the memristorcharacterized by a relationship between the charge q(t)\equiv \int_{-\infty}^{t} i(\tau) d \tau and the flux-linkage \varphi(t)\equiv \int_{- \infty}^{t} v(\tau) d \tau is introduced as the fourth basic circuit element. An electromagnetic field interpretation of this relationship in terms of a quasi-static expansion of Maxwell's equations is presented. Many circuit-theoretic properties of memistors are derived. It is shown that this element exhibits some peculiar behavior different from that exhibited by resistors, inductors, or capacitors. These properties lead to a number of unique applications which cannot be realized with RLC networks alone. Although a physical memristor device without internal power supply has not yet been discovered, operational laboratory models have been built with the help of active circuits. Experimental results are presented to demonstrate the properties and potential applications of memristors.
7,585 citations
"Memristor augmented ReRAM cell for ..." refers background in this paper
...INTRODUCTION In 1971 Leon Chua found the existence of fourth fundamental basic element, called Memristor [1]....
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...Leon Chua found the six different mathematical relation by using four fundamental elements such as current (i), voltage (v), flux ( ) and charge (q) [1] , all six mathematical relation shown in figure....
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TL;DR: It is shown that the proposed TEAM, ThrEshold Adaptive Memristor model is reasonably accurate and computationally efficient, and is more appropriate for circuit simulation than previously published models.
Abstract: Memristive devices are novel devices, which can be used in applications ranging from memory and logic to neuromorphic systems. A memristive device offers several advantages: nonvolatility, good scalability, effectively no leakage current, and compatibility with CMOS technology, both electrically and in terms of manufacturing. Several models for memristive devices have been developed and are discussed in this paper. Digital applications such as memory and logic require a model that is highly nonlinear, simple for calculations, and sufficiently accurate. In this paper, a new memristive device model is presented-TEAM, ThrEshold Adaptive Memristor model. This model is flexible and can be fit to any practical memristive device. Previously published models are compared in this paper to the proposed TEAM model. It is shown that the proposed model is reasonably accurate and computationally efficient, and is more appropriate for circuit simulation than previously published models.
666 citations
TL;DR: The VTEAM model extends the previously proposed ThrEshold Adaptive Memristor (TEAM) model, which describes current-controlled memristors and has similar advantages as the TEAM model, i.e., it is simple, general, and flexible, and can characterize different voltage-controlled Memristors.
Abstract: Memristors are novel electrical devices used for a variety of applications, including memory, logic circuits, and neuromorphic systems. Memristive technologies are attractive due to their nonvolatility, scalability, and compatibility with CMOS. Numerous physical experiments have shown the existence of a threshold voltage in some physical memristors. Additionally, as shown in this brief, some applications require voltage-controlled memristors to operate properly. In this brief, a Voltage ThrEshold Adaptive Memristor (VTEAM) model is proposed to describe the behavior of voltage-controlled memristors. The VTEAM model extends the previously proposed ThrEshold Adaptive Memristor (TEAM) model, which describes current-controlled memristors. The VTEAM model has similar advantages as the TEAM model, i.e., it is simple, general, and flexible, and can characterize different voltage-controlled memristors. The VTEAM model is accurate (below 1.5% in terms of the relative root-mean-square error) and computationally efficient as compared with existing memristor models and experimental results describing different memristive technologies.
564 citations
TL;DR: A new memristor crossbar architecture that is proposed for use in a high density cache design that has less than 10% of the write energy consumption and allows better performance along with lower system power when compared to the STT-MRAM and SRAM caches.
33 citations