Resistance state evolution under constant electric stress on a MoS<sub>2</sub> non-volatile resistive switching device

Wu, Xiaohan; Ge, Ruijing; Huang, Yifu; Akinwande, Deji; Lee, Jack C.

doi:10.1039/d0ra05209d

Cited by 9 publications

(11 citation statements)

References 33 publications

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“…Based on our previous studies on 2D TMD-based RRAM devices, a "conductive-point" model was proposed to illustrate the resistive switching mechanism (Wu et al, 2020;Wu et al, 2021). When applying external voltage bias, metal ions/atoms from TE can be dissociated triggered by the electrical field and adsorbed into the chalcogen defects in the ReSe 2 layer.…”

Section: Resultsmentioning

confidence: 99%

ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing

Huang

et al. 2021

Front. Nanotechnol.

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Resistive random-access memory (RRAM) devices have drawn increasing interest for the simplicity of its structure, low power consumption and applicability to neuromorphic computing. By combining analog computing and data storage at the device level, neuromorphic computing system has the potential to meet the demand of computing power in applications such as artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT). Monolayer rhenium diselenide (ReSe2), as a two-dimensional (2D) material, has been reported to exhibit non-volatile resistive switching (NVRS) behavior in RRAM devices with sub-nanometer active layer thickness. In this paper, we demonstrate stable multiple-step RESET in ReSe2 RRAM devices by applying different levels of DC electrical bias. Pulse measurement has been conducted to study the neuromorphic characteristics. Under different height of stimuli, the ReSe2 RRAM devices have been found to switch to different resistance states, which shows the potentiation of synaptic applications. Long-term potentiation (LTP) and depression (LTD) have been demonstrated with the gradual resistance switching behaviors observed in long-term plasticity programming. A Verilog-A model is proposed based on the multiple-step resistive switching behavior. By implementing the LTP/LTD parameters, an artificial neural network (ANN) is constructed for the demonstration of handwriting classification using Modified National Institute of Standards and Technology (MNIST) dataset.

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Section: Resultsmentioning

confidence: 99%

ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing

Huang

et al. 2021

Front. Nanotechnol.

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“…More details about the processes followed are given in the experimental section. Then, a ≈17 nm thick Au film is deposited everywhere on the samples via electron beam evaporation, using a deposition speed of 0.52 Å s −1 and a power of 11%; note that these parameters are similar to those often used in other studies, [31][32][33] and are considered to introduce low damage in the materials. [34] Figure 1f shows the optical microscope image of the h-BN flake locally protected with negative photoresist before the Au evaporation, and Figure 1g shows the optical microscope image of the h-BN flake protected with transferred Au electrode (on the top side of the flake) and with Ag ink (in the bottom side of the flake).…”

Section: Atomic Structure Of Multilayer H-bnmentioning

confidence: 99%

Defect‐Free Metal Deposition on 2D Materials via Inkjet Printing Technology

et al. 2021

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2D materials have many outstanding properties that make them attractive for the fabrication of electronic devices, such as high conductivity, flexibility, and transparency. However, integrating 2D materials in commercial devices and circuits is challenging because their structure and properties can be damaged during the fabrication process. Recent studies have demonstrated that standard metal deposition techniques (like electron beam evaporation and sputtering) significantly damage the atomic structure of 2D materials. Here it is shown that the deposition of metal via inkjet printing technology does not produce any observable damage in the atomic structure of ultrathin 2D materials, and it can keep a sharp interface. These conclusions are supported by abundant data obtained via atomistic simulations, transmission electron microscopy, nano-chemical metrology, and device characterization in a probe station. The results are important for the understanding of inkjet printing technology applied to 2D materials, and they could contribute to the better design and optimization of electronic devices and circuits.

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“…23 The diamond substrate was chosen due to its high thermal conductivity to sink the generated heat and maintain reliable device performance during the electrical measurements. 17,19,20 Figure 1c shows an optical microscopy (OM) image of the crossbar structure with an atomristor area of 1.0 × 1.0 μm 2 . The bottom Ag electrode was patterned and deposited onto the diamond substrate through electron beam (e-beam) lithography and evaporation systems.…”

Section: Introductionmentioning

confidence: 99%

Volatile and Nonvolatile Resistive Switching Coexistence in Conductive Point Hexagonal Boron Nitride Monolayer

Yang,

Liang,

Lee

et al. 2024

ACS Nano

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Recently, we demonstrated the nonvolatile resistive switching effects of metal−insulator−metal (MIM) atomristor structures based on two-dimensional (2D) monolayers. However, there are many remaining combinations between 2D monolayers and metal electrodes; hence, there is a need to further explore 2D resistance switching devices from material selections to future perspectives. This study investigated the volatile and nonvolatile switching coexistence of monolayer hexagonal boron nitride (h-BN) atomristors using top and bottom silver (Ag) metal electrodes. Utilizing an h-BN monolayer and Ag electrodes, we found that the transition between volatile and nonvolatile switching is attributed to the thickness/stiffness of chain-like conductive bridges between h-BN and Ag surfaces based on the current compliance and atomristor area. Computations indicate a "weak" bridge is responsible for volatile switching, while a "strong" bridge is formed for nonvolatile switching. The current compliance determines the number of Ag atoms that undergo dissociation at the electrode, while the atomristor area determines the degree of electric field localization that forms more stable conductive bridges. The findings of this study suggest that the h-BN atomristor using Ag electrodes shows promise as a potential solution to integrate both volatile neurons and nonvolatile synapses in a single neuromorphic crossbar array structure through electrical and dimensional designs.

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Resistance state evolution under constant electric stress on a MoS₂ non-volatile resistive switching device

Abstract: Constant voltage and current stress were applied on MoS2 resistive switching devices, showing unique behaviors explained by a modified conductive-bridge-like model.

Cited by 9 publications

References 33 publications

ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing

ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing

Defect‐Free Metal Deposition on 2D Materials via Inkjet Printing Technology

Volatile and Nonvolatile Resistive Switching Coexistence in Conductive Point Hexagonal Boron Nitride Monolayer

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Resistance state evolution under constant electric stress on a MoS2 non-volatile resistive switching device

Abstract: Constant voltage and current stress were applied on MoS2 resistive switching devices, showing unique behaviors explained by a modified conductive-bridge-like model.

Cited by 9 publications

References 33 publications

ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing

ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing

Defect‐Free Metal Deposition on 2D Materials via Inkjet Printing Technology

Volatile and Nonvolatile Resistive Switching Coexistence in Conductive Point Hexagonal Boron Nitride Monolayer

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Resistance state evolution under constant electric stress on a MoS₂ non-volatile resistive switching device