Nonlinear Dynamics of a Locally-Active Memristor

Ascoli, Alon; Slesazeck, Stefan; Mähne, Hannes; Tetzlaff, Ronald; Mikolajick, Thomas

doi:10.1109/tcsi.2015.2413152

Cited by 160 publications

(97 citation statements)

References 19 publications

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“…the Niobium dioxide-based nanostructure manufactured at HP labs [16]. The local activity of these memristors and the complex dynamics which may emerge in circuits employing them (for example the oscillatory behavior of a simple locally active memristor circuit built at NaMLab is investigated in [17]) may lead to the development of new circuits [18] which could outperform state-of-the-art electronic systems or complement their functionalities. This wide plethora of interesting applications urgently requires the developments of accurate mathematical models in order to uncover the full potential of memristors in the electronics of the future.…”

Section: Introductionmentioning

confidence: 99%

Robust Simulation of a TaO Memristor Model

Ascoli¹,

Tetzlaff²,

Chua³

2015

RADIOENGINEERING

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Abstract. This work presents a continuous and differentiable approximation of a Tantalum oxide memristor model which is suited for robust numerical simulations in software. The original model was recently developed at Hewlett Packard labs on the basis of experiments carried out on a memristor manufactured in house. The Hewlett Packard model of the nano-scale device is accurate and may be taken as reference for a deep investigation of the capabilities of the memristor based on Tantalum oxide. However, the model contains discontinuous and piecewise differentiable functions respectively in state equation and Ohm's based law. Numerical integration of the differential algebraic equation set may be significantly facilitated under substitution of these functions with appropriate continuous and differentiable approximations. A detailed investigation of classes of possible continuous and differentiable kernels for the approximation of the discontinuous and piecewise differentiable functions in the original model led to the choice of near optimal candidates. The resulting continuous and differentiable DAE set captures accurately the dynamics of the original model, delivers well-behaved numerical solutions in software, and may be integrated into a commercially-available circuit simulator.

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Section: Introductionmentioning

confidence: 99%

Robust Simulation of a TaO Memristor Model

Ascoli¹,

Tetzlaff²,

Chua³

2015

RADIOENGINEERING

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“…With the increase in the number of rows, the read latency shortens because of the decrease of R eq , which is dominant in 4 . By adding more rows, 3 becomes dominant, and the read latency increases, but not as much, as shown in Figure 8. R on and R eq are never multiplied by N; therefore, increasing the number of columns has a negligible effect on the total read latency, as shown in Figure 9.…”

mentioning

confidence: 93%

“…The unique properties of the memristor can be beneficial for many applications, such as threshold switch [3], dynamic load [4] and memory, which is considered the primary application for these devices. Resistive random-access memory [5][6][7][8][9][10][11] is one interesting type of memristive device [12].…”

Section: Introductionmentioning

confidence: 99%

“…where 1 is the row delay, 2 is the delay of the selected memristor, 3 is the approximation of the column delay using the partial sum of harmonic series (0.577 is the Euler-Mascheroni constant) [41], and 4 is the sense circuit delay, as illustrated in Figure 6. The calculation method is detailed in Appendix A.…”

mentioning

confidence: 99%

“…When the rule in (6) is kept, 3 and 4 are the dominant factors in the set { i }, i = {1, 2, 3, 4}. With the increase in the number of rows, the read latency shortens because of the decrease of R eq , which is dominant in 4 .…”

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confidence: 99%

See 2 more Smart Citations

Analysis of the row grounding technique in a memristor‐based crossbar array

Dozortsev

Goldshtein

Kvatinsky

2017

Circuit Theory & Apps

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SUMMARYUsing memristive devices within a crossbar array could pave the way for memories with higher density and speed than state-of-the-art Flash memory, while maintaining relatively low energy. However, memristive crossbar arrays have great difficulty distinguishing logical states because of sneak path currents. The row grounding technique eliminates the sneak path effect, allowing reliable sampling of the memristor state.In this paper, we analyze the row grounding technique and propose several methods and constraints for the design of memristive crossbar arrays. When the row grounding technique is used for these arrays, our analysis shows that increasing the number of rows can help reduce read latency and energy, in contrast to the case of capacitive memory arrays. Simulation results confirm the theoretical analysis proposed in this paper.

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Visualizing Thermally Activated Memristive Switching in Percolating Networks of Solution‐Processed 2D Semiconductors

Sangwan

Rangnekar

Kang

et al. 2021

Adv Funct Materials

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Memristive systems present a low-power alternative to silicon-based electronics for neuromorphic and in-memory computation. 2D materials have been increasingly explored for memristive applications due to their novel biomimetic functions, ultrathin geometry for ultimate scaling limits, and potential for fabricating large-area, flexible, and printed neuromorphic devices. While the switching mechanism in memristors based on single 2D nanosheets is similar to conventional oxide memristors, the switching mechanism in nanosheet composite films is complicated by the interplay of multiple physical processes and the inaccessibility of the active area in a twoterminal vertical geometry. Here, the authors report thermally activated memristors fabricated from percolating networks of diverse solution-processed 2D semiconductors including MoS 2 , ReS 2 , WS 2 , and InSe. The mechanisms underlying threshold switching and negative differential resistance are elucidated by designing large-area lateral memristors that allow the direct observation of filament and dendrite formation using in situ spatially resolved optical, chemical, and thermal analyses. The high switching ratios (up to 10 3 ) that are achieved at low fields (≈4 kV cm −1 ) are explained by thermally assisted electrical discharge that preferentially occurs at the sharp edges of 2D nanosheets. Overall, this work establishes percolating networks of solutionprocessed 2D semiconductors as a platform for neuromorphic architectures.

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Nonlinear Dynamics of a Locally-Active Memristor

Cited by 160 publications

References 19 publications

Robust Simulation of a TaO Memristor Model

Robust Simulation of a TaO Memristor Model

Analysis of the row grounding technique in a memristor‐based crossbar array

Visualizing Thermally Activated Memristive Switching in Percolating Networks of Solution‐Processed 2D Semiconductors

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