A ZnO-rGO composite thin film discrete memristor

Khanal, Gaurav Mani; Cardarilli, G.C.; Chakraborty, Abhisek; Acciarito, Simone; Mulla, Mohammad Yusuf; Nunzio, Luca Di; Fazzolari, Rocco; Re, M.

doi:10.1109/smelec.2016.7573608

Cited by 19 publications

(10 citation statements)

References 15 publications

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“…This high current could be due to the large TE area; indeed, the dependency of memristive device operation on the top electrode material and area is well studied and documented [34,[45][46][47][48][49]. The I-V characteristics of the device depend on the input voltage sweep rate [22]. The non-zero crossing I-V implies that there is some charge stored in the device, suggesting that the device has memory impedance at the nano scale, which naturally coexists with memristive behavior.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, many reports [19][20][21][22][23][24] have experimentally demonstrated that memristive, memcapacitive, and meminductive behavior coexist in the same single nano-sized memristive device. Published reports have shown and argued that it is possible to achieve memristive and memimpedance behavior on a single nano-device by varying the frequency of the input signal or by varying the input sweep rate (V/s).…”

Section: Introductionmentioning

confidence: 99%

“…Among the available materials for memristor and memristive devices, zinc oxide (ZnO) and its composite has some unique advantages: nontoxic and easy synthesis, high ion mobility, excellent resistance switching property, and relatively low set and reset voltages. In recent years, ZnO and its composites have been extensively used to fabricate memristive devices [19,22,27,28]. In previous works, the high-temperature thermal annealing process was used for creating vacancies within the ZnO thin film [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Memristive and Memory Impedance Behavior in a Photo-Annealed ZnO–rGO Thin-Film Device

et al. 2020

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An oxygen-rich ZnO-reduced graphene oxide (rGO) thin film was synthesized using a photo-annealing technique from zinc precursor (ZnO)–graphene oxide (GO) sol–gel solution. X-ray diffraction (XRD) results show a clear characteristic peak corresponding to rGO. The scanning electron microscope (SEM) image of the prepared thin film shows an evenly distributed wrinkled surface structure. Transition Metal Oxide (TMO)-based memristive devices are nominees for beyond CMOS Non-Volatile Memory (NVRAM) devices. The two-terminal Metal–TMO (Insulator)–Metal (MIM) memristive device is fabricated using a synthesized ZnO–rGO as an active layer on fluorine-doped tin oxide (FTO)-coated glass substrate. Aluminum (Al) is deposited as a top metal contact on the ZnO–rGO active layer to complete the device. Photo annealing was used to reduce the GO to rGO to make the proposed method suitable for fabricating ZnO–rGO thin-film devices on flexible substrates. The electrical characterization of the Al–ZnO–rGO–FTO device confirms the coexistence of memristive and memimpedance characteristics. The coexistence of memory resistance and memory impedance in the same device could be valuable for developing novel programmable analog filters and self-resonating circuits and systems.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Memristive and Memory Impedance Behavior in a Photo-Annealed ZnO–rGO Thin-Film Device

et al. 2020

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“…1. Same authors have presented a detailed description of the sol–gel synthesis of ZnO–rGO composite film and ZnO–rGO memristor characterisation in [11]. The conduction mechanism of this device is filamentary based, where formation and annihilation of the filament made of free oxygen vacancies within the thin film switch the device to low resistance state (LRS) or high resistance state (HRS) (see Figs.…”

Section: Methodsmentioning

confidence: 99%

Synaptic behaviour in ZnO–rGO composites thin film memristor

Khanal¹,

Acciarito²,

Cardarilli³

et al. 2017

Electron. lett.

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A zinc oxide (ZnO)-reduced graphene oxide (rGO) composite thin film memristive device is reported. Further, it has been shown that it is possible to implement Hebbian learning rules like, the spiketiming-dependent plasticity, using this device. Furthermore, a circuit on PCB is developed; this circuit can imitate the biological spike firing scheme and activate the memristor synapse. The fabricated device along with the custom made circuit can be extended for developing future neuromorphic circuit applications.

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“…Over the years, the dynamic of the heartbeat was analyzed through both mathematical model and time series analysis. In recent years, further help has come from the spread of various artificial intelligence techniques [1]- [15], many of which are based on neural networks that use the latest technologies [16]- [21]. Despite the wide use of the time series for the study of ECG signal, the complexity, nonlinearity, and nonstationarity of the cardiovascular system make common the use of nonlinear signal analysis for the modeling of heart activity [22].…”

Section: Introductionmentioning

confidence: 99%

Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features

Silvestri¹,

Acciarito²,

Khanal³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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The mathematical model describes the electrical and mechanical activity of the cardiac conduction system thought set of differential equations. By changing the value of parameters included in these equations, it is possible to change the amplitude and the period of ECG waves. Although this model is a powerful tool for modeling the electrical activity of the heart, its use is often limited to those familiar with the differential equations that describe the system. The purpose of this work is to provide a system that allows generating an ECG signal using Ryzhii model without knowing the details of differential equations. First, we provide the relationships between the ECG wave features and the model parameters; then we generalize them through fitting neural networks. Finally, putting in series fitting neural network and heart model, we provide a system that allows generating a synthetic signal by setting as input only the morphological ECG feature. We computed numerical simulation in Simulink environment and implemented the fitting neural networks in Matlab. Results show that non-linear trends characterize the correlation functions between ECG morphological features and model parameters and that the fitting neural networks can generalized this trend by providing the model parameters given in input the respectively ECG feature.

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A ZnO-rGO composite thin film discrete memristor

Cited by 19 publications

References 15 publications

Memristive and Memory Impedance Behavior in a Photo-Annealed ZnO–rGO Thin-Film Device

Memristive and Memory Impedance Behavior in a Photo-Annealed ZnO–rGO Thin-Film Device

Synaptic behaviour in ZnO–rGO composites thin film memristor

Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features

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