ZnO-rGO Composite Thin Film Resistive Switching Device: Emulating Biological Synapse Behavior

Khanal, Gaurav Mani; Acciarito, Simone; Cardarilli, G.C.; Chakraborty, Abhisek; Nunzio, Luca Di; Fazzolari, Rocco; Cristini, A.; Susi, Gianluca; Re, M.

doi:10.1007/978-3-319-55071-8_15

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…It was confirmed in [219] that proposed a ZnO-based RRAM structure with a thin rGO coating layer of 13 nm. The insertion of the rGO was revealed to enhance the crystallinity and boost the device switching dynamics [215], [220], [221]. Cardarilli et al [222] demonstrates the diffraction patterns of ZnO with and without rGO as shown in Fig.…”

Section: E Hybrid Layer and Its Significance In Zno Based Rrammentioning

confidence: 99%

“…22(e) and (f) has the most stable switching curve. However, inserting a capping layer of rGO in the structure between the ZnO and electrodes may enhance the overall switching characteristics due to the oxygen storage reservoir nature of the rGO layer [215], [218], [221]. Still, more investigations are needed to elucidate the influence of GO capping layer properties on the ZnO switching characteristics under the various bottom and top electrodes.…”

Section: E Hybrid Layer and Its Significance In Zno Based Rrammentioning

confidence: 99%

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ZnO Based Resistive Random Access Memory Device: A Prospective Multifunctional Next-Generation Memory

et al. 2021

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Numerous works that have demonstrated the study and enhancement of switching properties of ZnO-based RRAM devices are discussed. Several native point defects that have a direct or indirect effect on ZnO are discussed. The use of doping elements, multi-layered structures, suitable bottom and top electrodes, controlling the deposition materials, and the impact of hybrid structure for enhancing the switching dynamics are discussed. The potentials of ZnO-based RRAM for invisible and bendable devices are also covered. ZnO-based RRAM has the potential for possible application in bio-inspired cognitive computational systems. Thus, the synapse capability of ZnO is presented. The sneak-path current issue also besets ZnO-based RRAM crossbar array architecture. Hence, various attempts to subdue the bottleneck have been shown and discussed in this article. Interestingly, ZnO provides not only helpful memory features. However, it demonstrates the ability to be used in nonvolatile multifunctional memory devices. Also, this review covers various issues like the effect of electrodes, interfacial layers, proper switching layers, appropriate fabrication techniques, and proper annealing settings. These may offer a valuable understanding of the study and development of ZnO-based RRAM and should be an avenue for overcoming RRAM challenges.

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Section: E Hybrid Layer and Its Significance In Zno Based Rrammentioning

confidence: 99%

Section: E Hybrid Layer and Its Significance In Zno Based Rrammentioning

confidence: 99%

ZnO Based Resistive Random Access Memory Device: A Prospective Multifunctional Next-Generation Memory

et al. 2021

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“…The parameters a i , u ij , f i , d i , e i , were choosen in order to obtain intrinsic oscillation rates of 70 bpm, 50 bpm, 35 bpm and 35 bpm for uncoupled SA, AV, RB and LB, respectively. In particular, we used the following experimental parameters: a 1 = 40, a 2 = 50, a 3 = 50, a 1 = 40, u 11 = 0.83, u 21 The activation currents I i represent the coupling between the SA and AT muscle and between HP pacemaker and VN muscles. Concerning the Ryzhii model [30], we adjusted the activation currents for the QRS complex and T wave to consider HP oscillator composed by the RB and LB oscillators:…”

Section: A Electrocardiographic Signalmentioning

confidence: 99%

“…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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“…In the last few years, Machine Learning (ML) gained an important role in several fields that as health, computer vision, and communications energy [1]- [14]. The availability of increasingly high computational power and the introduction of new technologies have increased the interest in ML [15]- [27]. The mix of these two aspects provides the possibility to implement complex algorithms without compromising real-time computation on embedded devices.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Hand-written Number Recognition Based on CNN

Giardino

Matta

Silvestri

et al. 2019

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

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Convolutional Neural Networks (CNNs) are the state-of-the-art in computer vision for different purposes such as image and video classification, recommender systems and natural language processing. The connectivity pattern between CNNs neurons is inspired by the structure of the animal visual cortex. In order to allow the processing, they are realized with multiple parallel 2dimensional FIR filters that convolve the input signal with the learned feature maps. For this reason, a CNN implementation requires highly parallel computations that cannot be achieved using traditional general-purpose processors, which is why they benefit from a very significant speed-up when mapped and run on Field Programmable Gate Arrays (FPGAs). This is because FPGAs offer the capability to design full customizable hardware architectures, providing high flexibility and the availability of hundreds to thousands of on-chip Digital Signal Processing (DSP) blocks. This paper presents an FPGA implementation of a handwritten number recognition system based on CNN. The system has been characterized in terms of classification accuracy, area, speed, and power consumption. The neural network was implemented on a Xilinx XC7A100T FPGA, and it uses 29.69% of Slice LUTs, 4.42% of slice registers and 52.50% block RAMs. We designed the system using a 9-bit representation that allows for avoiding the use of DSP. For this reason, multipliers are implemented using LUTs. The proposed architecture can be easily scaled on different FPGA devices thank its regularity. CNN can reach a classification accuracy of 90%.

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ZnO-rGO Composite Thin Film Resistive Switching Device: Emulating Biological Synapse Behavior

Cited by 5 publications

References 14 publications

ZnO Based Resistive Random Access Memory Device: A Prospective Multifunctional Next-Generation Memory

ZnO Based Resistive Random Access Memory Device: A Prospective Multifunctional Next-Generation Memory

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

FPGA Implementation of Hand-written Number Recognition Based on CNN

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