An Elementary Note on Skin Hydration Measurement Using Memristive Effect

Dongale, Tukaram D.

doi:10.5121/hiij.2013.2102

Cited by 12 publications

(11 citation statements)

References 13 publications

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“…Moreover, the passivity and nonlinearity are some of the important characteristics of the memristor, which leads to its usage in the applications of diversified domains such as biomedical, resistive random access memory (RRAM), neural computing, nonlinear dynamics, neuromorphic computing realm etc. as reported widely in the literature [ 3 – 10 ]. As these applications are important so is the accurate modelling of nonlinear memristor which has been the very basis of the scientific investigations.…”

Section: Introductionmentioning

confidence: 86%

TiO2 based nanostructured memristor for RRAM and neuromorphic applications: a simulation approach

et al. 2016

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We report simulation of nanostructured memristor device using piecewise linear and nonlinear window functions for RRAM and neuromorphic applications. The linear drift model of memristor has been exploited for the simulation purpose with the linear and non-linear window function as the mathematical and scripting basis. The results evidences that the piecewise linear window function can aptly simulate the memristor characteristics pertaining to RRAM application. However, the nonlinear window function could exhibit the nonlinear phenomenon in simulation only at the lower magnitude of control parameter. This has motivated us to propose a new nonlinear window function for emulating the simulation model of the memristor. Interestingly, the proposed window function is scalable up to f(x) = 1 and exhibits the nonlinear behavior at higher magnitude of control parameter. Moreover, the simulation results of proposed nonlinear window function are encouraging and reveals the smooth nonlinear change from LRS to HRS and vice versa and therefore useful for the neuromorphic applications.

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Section: Introductionmentioning

confidence: 86%

TiO2 based nanostructured memristor for RRAM and neuromorphic applications: a simulation approach

et al. 2016

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“…The device shows the change in resistance state depending upon how much current flow through the device [2]. This unique property make it's a valuable circuit element for many applications [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Memristor Device Performancementioning

confidence: 99%

Development of Ag/ZnO/FTO thin film memristor using aqueous chemical route

Dongale

Khot

Mali

et al. 2015

Materials Science in Semiconductor Processing

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“…The inherent memory property of memristor is distinctly observed in the nanoscale and therefore, it is considered as a strong candidate for the next generation memories [3]. Along with its applications in memory, there are many interesting applications explored around memristors such as neuromorphic computations [4], in-memory computing [5], biomedical application [6] and much more.…”

Section: Introductionmentioning

confidence: 99%

Effect of write voltage and frequency on the reliability aspects of memristor-based RRAM

et al. 2017

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In this paper, we report the effect of the write voltage and frequency on memristor-based resistive random access memory (RRAM). The above-said parameters have been investigated on the linear drift model of the memristor. With a variation of write voltage from 0.2 to 1.2 V and a subsequent frequency modulation from 1, 2, 4, 10, 100 and 200 Hz, the corresponding effects on memory window, low resistance state (LRS) and high resistance state (HRS) have been reported. Thus, the lifetime (s) reliability analysis of memristor-based RRAM is carried out using the above results. It is found that the HRS is independent of the write voltage, whereas LRS shows dependency on write voltage and frequency. The simulation results showcase the fact that the memristor possesses higher memory window and lifetime (s) in the higher voltage with lower frequency region, which has been attributed to less data losses in the memory architecture.

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An Elementary Note on Skin Hydration Measurement Using Memristive Effect

Abstract: ABSTRACT

Cited by 12 publications

References 13 publications

TiO2 based nanostructured memristor for RRAM and neuromorphic applications: a simulation approach

TiO2 based nanostructured memristor for RRAM and neuromorphic applications: a simulation approach

Development of Ag/ZnO/FTO thin film memristor using aqueous chemical route

Effect of write voltage and frequency on the reliability aspects of memristor-based RRAM

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