The bipolar and unipolar reversible behavior on the forgetting memristor model

Chen, Ling; Li, Chuandong; Huang, Tingwen; Hu, Xiaofang; Chen, Yiran

doi:10.1016/j.neucom.2015.06.067

Cited by 25 publications

(14 citation statements)

References 37 publications

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“…And, the memristor model response to a reversible bipolar and unipolar behavior is shown in Figure 13.We can conclude that our simulation results are consistent and in very good qualitative agreement with the results already published in [15]. A detailed comparison between our work model and other popular memristor models (the Chua [1], the Strukov (HP) [3], Vourkas [45], and the Chen [15]) is shown in Table 1. We can notice that the SPICE model gets a special advantage on describing various memristors models with an average number of parameters, and for its flexibility, and low complexity.…”

supporting

confidence: 86%

“…Those results describe the static and dynamical characteristics of the model. Thus, we prove that the SPICE model fits well with the characterization data of memristors defined in [8,15,17,25,[39][40][41][42][43][44]. The polarization voltages studied are either sinusoidal pulses or repetitive DC sweeping voltage to represent the different switching resistive levels of the memristor.…”

Section: Correlation Of Different Memristive Devicessupporting

confidence: 54%

“…The wide variety in memristor structure and composition has led to the development of many different memristor modeling techniques. Some of them have been designed to represent a specific device for a specific type of application, such as AHaH [12], ANN [15,16], Slime mold [17], and neuromorphic applications [5]. Implementation of the memristor could be generated on several tools of simulation, such as SPICE [18][19][20][21][22][23][24][25], Matlab [26][27][28][29][30][31][32], Verilog-A [33], and VHDL-AMS [34][35][36][37].…”

Section: Theoretical Principlesmentioning

confidence: 99%

“…Meanwhile, we also simulated the model with a square wave excitation shown in Figure 9. This excitation method is used as a learning method, which presents the behavior of this model as an artificial neural network ANN [15]. We follow the learning experience carried out in [16].…”

Section: Memristive Device For Ann Learning Applicationmentioning

confidence: 99%

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Coexistence of Bipolar and Unipolar Memristor Switching Behavior

Ghedira¹,

Rziga²,

Mbarek³

et al. 2019

Memristors - Circuits and Applications of Memristor Devices [Working Title]

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The memristor has been theoretically investigated as one of the fundamental electrical elements by Pr. Leon Chua in 1971. Meanwhile, its electrical characteristics are not yet fully understood. The nonlinear characteristics and the ability to examine large-scale amounts of storing data of this device reveal an interesting development in emerging electronic systems. Research on memristor modeling based on SPICE tools has grown rapidly. This leads us to study the behavior of such devices. Our aim is to simulate different types of memristor behavior. The adjustment of the model is based on the implementation of several parameters, which enables the switching of this device. In this chapter, we prove the flexibility and the correlation of memristor model with different memristive characterization data, by applying different voltage bias, sinusoidal and with a repetitive sweeping. Moreover, we demonstrate the memristor behavior as four types of switching. This includes bipolar switching, unipolar switching, bipolar switching with forgetting effect, and a reversible process between bipolar and unipolar switching. In order to validate this study, we compare our simulation results with experimental data and we prove a good agreement. The SPICE model used in our simulations shows a special advantage for its flexibility and simplicity.

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supporting

confidence: 86%

Section: Correlation Of Different Memristive Devicessupporting

confidence: 54%

Section: Theoretical Principlesmentioning

confidence: 99%

Section: Memristive Device For Ann Learning Applicationmentioning

confidence: 99%

See 2 more Smart Citations

Coexistence of Bipolar and Unipolar Memristor Switching Behavior

Ghedira¹,

Rziga²,

Mbarek³

et al. 2019

Memristors - Circuits and Applications of Memristor Devices [Working Title]

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“…The study was motivated by the previous reports investigating the effect of tuning electrochemical parameters on anodic oxide growth [ 20 , 21 , 22 , 23 ] or memristive behavior, confirming the influence of electrolyte selection as the most relevant [ 24 , 25 ]. Since unipolar devices can be used for dense integration on diode selectors and bipolar ones show higher device lifetime [ 14 ], the controllable switching of the Nb anodic oxide memristors may be beneficial regarding the range of their possible applications [ 1 , 26 , 27 , 28 , 29 ]. Finally, the switching mechanism based on CF formation, influenced by their size and positioning, is suggested for devices showing reversible switching in unipolar and bipolar mode based on TEM investigation.…”

Section: Introductionmentioning

confidence: 99%

Impact of Electrolyte Incorporation in Anodized Niobium on Its Resistive Switching

et al. 2022

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The aim of this study was to develop memristors based on Nb2O5 grown by a simple and inexpensive electrochemical anodization process. It was confirmed that the electrolyte selection plays a crucial role in resistive switching due to electrolyte species incorporation in oxide, thus influencing the formation of conductive filaments. Anodic memristors grown in phosphate buffer showed improved electrical characteristics, while those formed in citrated buffer exhibited excellent memory capabilities. The chemical composition of oxides was successfully determined using HAXPES, while their phase composition and crystal structure with conductive filaments was assessed by TEM at the nanoscale. Overall, understanding the switching mechanism leads towards a wide range of possible applications for Nb memristors either as selector devices or nonvolatile memories.

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