Synaptic behaviors and modeling of a metal oxide memristive device

Chang, Ting Chang; Jo, Sung‐Hyun; Kim, Kuk-Hwan; Sheridan, Patrick; Gaba, Siddharth; Lü, Wei

doi:10.1007/s00339-011-6296-1

Cited by 387 publications

(338 citation statements)

References 18 publications

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“…Alternatively, it is again straightforward to show that the state and input-output equations in Case Study #2 define another memristor. We can also show that: [7], the analytic signal in (c) follows [38], while the piecewise signal in (c) mimics [35] …”

Section: Regarding Variable(s) In State Vector Ymentioning

confidence: 67%

“…For comparison purposes, results from three nano-device models (two memristors and one memristive system in [7,42,43]) are also summarized and discussed. The three nano-device models, designated herein as Case Studies #1, 2 and 3 (see "Case studies from nano-field" of Appendix 3), can be viewed as mem-dashpots by means of the ForceVoltage Analogy.…”

Section: Overview Of Mem-dashpot Case Studiesmentioning

confidence: 99%

“…The three nano-device models, designated herein as Case Studies #1, 2 and 3 (see "Case studies from nano-field" of Appendix 3), can be viewed as mem-dashpots by means of the ForceVoltage Analogy. Instead of using the Simulation Program with Integrated Circuit Emphasis (SPICE) as in Chang et al [7], MATLAB TM was used for all computa- Fig. 11 One element contrasted with one system model (see Table 12) to illustrate the behavior of (x, r ) in the third quadrant; x(t) = A sin(ωt) with A = 1 and ω = 1 This section focuses on the governing state and input-output equations.…”

Section: Overview Of Mem-dashpot Case Studiesmentioning

confidence: 99%

“…where y = w D , which may have a physical meaning of being a normalized width according to Chang et al [7]. It can be seen that y,ẏ-joined with v-form a memristor (not a memristive system).…”

Section: Regarding Variable(s) In State Vector Ymentioning

confidence: 99%

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Dual input–output pairs for modeling hysteresis inspired by mem-models

et al. 2017

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Section: Regarding Variable(s) In State Vector Ymentioning

confidence: 67%

Section: Overview Of Mem-dashpot Case Studiesmentioning

confidence: 99%

Section: Overview Of Mem-dashpot Case Studiesmentioning

confidence: 99%

Section: Regarding Variable(s) In State Vector Ymentioning

confidence: 99%

See 2 more Smart Citations

Dual input–output pairs for modeling hysteresis inspired by mem-models

et al. 2017

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“…A boarder class called memristive system was proposed later by Chua, in which, memristor is just a special case [10]. Beyond the postulate, the memristor has been found in nano scale by several research groups utilizing different materials such as the titanium-dioxide based memristor [24], the ferroelectric memristor [5], the tungsten-oxide based memristor [4] and the diamond-like carbon based memristor [6]. The memristor with staircase behavior is firstly introduced by Chua [8] and emulated by non-linear resistors and Zener diodes.…”

Section: Introductionmentioning

confidence: 99%

Investigations of the staircase memristor model and applications of memristor-based local connections

Yang

Chen

Wang

2016

Analog Integr Circ Sig Process

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The memristor is postulated by Chua (IEEE Trans Circuit Theory, 18(5): [507][508][509][510][511][512][513][514][515][516][517][518][519] 1971) as the fourth fundamental passive circuit element and experimentally validated by HP labs in 2008. It is an emerging device in nano technology that provides low power consumption as well as high density. In this paper, the staircase memristor model is discussed and investigated with the HP memristor model. By connecting HP memristor models in series or parallel, a staircase memristor model could be constructed and demonstrate staircase behavior. By comparing the staircase memristor model to the general HP memristor model, distinctions between them are demonstrated and this lends themselves to different applications. Further to the memristor-based cellular neural network (CNN), the structure is modified and applied to the echo state network (ESN) where memristors are used as local connections. By this means, the ESN benefits from the simple structure, non-volatility and low power feature of memristors and therefore the complexity and size of the original ESN architecture can be reduced. Meanwhile, the simpler structure still has satisfactory performance in applications compared with the original ESN.

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