Electroforming free controlled bipolar resistive switching in Al/CoFe2O4/FTO device with self-compliance effect

Munjal, Sandeep; Khare, Neeraj

doi:10.1063/1.4998401

Cited by 44 publications

(17 citation statements)

References 30 publications

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“…The 2D band was used to characterize changes in the number of GO layers. The 2D peak of CS-GO shifted from 2656 cm -1 to 2682 cm -1 in the GO and CS-GO spectra, which showed the change in the GO layer as it was embedded into the CS matrix [11]. The features of both CS and GO were detected in the CS-GO spectrum.…”

Section: Resultsmentioning

confidence: 91%

“…Resistive random access memory (RRAM) is one candidate for next-generation information storage due to its high speed, long retention time with low power consumption, and simple structure [2,3]. Different materials such as organics, inorganics, transition metal dichalcogenides, metal-organic frameworks, and hybrid materials [4][5][6][7][8][9][10][11][12] have been utilized in RRAM devices to demonstrate their potential applications in data storage.…”

Section: Introductionmentioning

confidence: 99%

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The Resistive Switching Behavior of Al/Chitosan-Graphene Oxide/FTO Structure

Pham

2021

Journal of Nanomaterials

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Resistive random access memory (RRAM) is emerging as a new class of nonvolatile memory that offers promising electronic properties and simple metal-insulator-metal (MIM) structures for sandwich layers, such as organics, inorganics, and hybrid materials. Hybrid structures have attracted much interest recently because of their advantageous properties. The combination of chitosan (CS) and graphene oxide (GO) acts as switching layers in the Al/CS-GO/FTO RRAM structure it is studied with bipolar switching behavior at approximately 102 ON/OFF ratios during 100 cycles. This hybrid interaction is identified by shifts in the D, G, and 2D bands using Raman spectroscopy. The conduction mechanism is proposed to be a space-charge-limited conduction (SCLC) mechanism and trap-assisted tunneling conduction mechanism in the ON and OFF states, respectively. The trapped and detrapped electrons move through the trap sites with external electric fields, and this movement is responsible for the switching mechanism of the CS-GO nanocomposite memory device.

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Section: Resultsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

The Resistive Switching Behavior of Al/Chitosan-Graphene Oxide/FTO Structure

Pham

2021

Journal of Nanomaterials

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“…The smaller the gap is, the smaller the electroforming voltage. Nevertheless, it has been shown that resistive switching can also be initiated without electroforming ( 43 – 45 ). In the feedback-controlled EB protocol that we use, a predetermined forming voltage was not applied.…”

Section: Effect Of the Environment In The Conductance Histogrammentioning

confidence: 99%

Statistical signature of electrobreakdown in graphene nanojunctions

Evangeli

Tewari

Kruip

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Controlled electrobreakdown of graphene is important for the fabrication of stable nanometer-size tunnel gaps, large-scale graphene quantum dots, and nanoscale resistive switches, etc. However, owing to the complex thermal, electronic, and electrochemical processes at the nanoscale that dictate the rupture of graphene, it is difficult to generate conclusions from individual devices. We describe here a way to explore the statistical signature of the graphene electrobreakdown process. Such analysis tells us that feedback-controlled electrobreakdown of graphene in the air first shows signs of joule heating-induced cleaning followed by rupturing of the graphene lattice that is manifested by the lowering of its conductance. We show that when the conductance of the graphene becomes smaller than around 0.1 G 0 , the effective graphene notch width starts to decrease exponentially slower with time. Further, we show how this signature gets modified as we change the environment and or the substrate. Using statistical analysis, we show that the electrobreakdown under a high vacuum could lead to substrate modification and resistive-switching behavior, without the application of any electroforming voltage. This is attributed to the formation of a semiconducting filament that makes a Schottky barrier with the graphene. We also provide here the statistically extracted Schottky barrier threshold voltages for various substrate studies. Such analysis not only gives a better understanding of the electrobreakdown of graphene but also can serve as a tool in the future for single-molecule diagnostics.

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“…[108][109][110] Meanwhile, controlling the concentration of defects (such as vacancies) can also result in electroforming free behavior. [22,111] Instead of forming the conductive path by the electric field during the first operation, the preformed conductive path during the manufacturing process can be used to avoid the electroforming process. [112,113] The cycle-to-cycle variation is associated with the unstable CFs or stochastic formation of new CFs.…”

Section: Device Variationmentioning

confidence: 99%

Hardware Implementation of Neuromorphic Computing Using Large‐Scale Memristor Crossbar Arrays

Ang

2020

Advanced Intelligent Systems

138

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Brain‐inspired neuromorphic computing is a new paradigm that holds great potential to overcome the intrinsic energy and speed issues of traditional von Neumann based computing architecture. With the ability to perform vector‐matrix multiplications and flexible tunable conductance, the memristor crossbar array (CBA) structure is one of the most promising candidates to realize neural cognitive systems. The boom in the development of memristive synapses and neurons has propelled the developments of artificial neural networks (ANNs) to emulate the highly hierarchically organized network of human brain in the past decade. To achieve this, realizing large scale, high‐density memristive CBAs is a prerequisite to constructing complex ANNs. Herein, the stringent requirements in device performance and array parameters for hardware ANNs are analyzed, and the efforts in addressing the associated challenges are discussed. Recent progress on the experimental demonstration of neuromorphic computing systems (NCSs) is presented. Recommendations for further performance optimization at the device, circuit, and algorithm levels are proposed. This Report serves as a guide for the hardware implementation of NCS based on large‐scale CBAs.

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Electroforming free controlled bipolar resistive switching in Al/CoFe2O4/FTO device with self-compliance effect

Cited by 44 publications

References 30 publications

The Resistive Switching Behavior of Al/Chitosan-Graphene Oxide/FTO Structure

The Resistive Switching Behavior of Al/Chitosan-Graphene Oxide/FTO Structure

Statistical signature of electrobreakdown in graphene nanojunctions

Hardware Implementation of Neuromorphic Computing Using Large‐Scale Memristor Crossbar Arrays

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