Graphene Oxide: Graphene Quantum Dot Nanocomposite for Better Memristic Switching Behaviors

Li, Lei

doi:10.3390/nano10081448

Cited by 14 publications

(6 citation statements)

References 38 publications

(41 reference statements)

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“…Resistive switching random access memory (RRAM) can act as a synapse in a neuromorphic chip because of its low-power operation [7], fast switching time [8], high-density integration [9], and multi-level cells (MLC) with analogue switching [10][11][12][13][14]. The various resistive switching characteristics are achieved using a dielectric material and metal electrodes [15][16][17][18][19][20][21][22][23][24][25][26][27]. Additionally, the switching type can be changed depending on the operation conditions, such as the current and voltage levels [28].…”

Section: Introductionmentioning

confidence: 99%

Synaptic Characteristics from Homogeneous Resistive Switching in Pt/Al2O3/TiN Stack

Ryu

Kim

2020

Nanomaterials

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In this work, we propose three types of resistive switching behaviors by controlling operation conditions. We confirmed well-known filamentary switching in Al2O3-based resistive switching memory using the conventional device working operation with a forming process. Here, filamentary switching can be classified into two types depending on the compliance current. On top of that, the homogeneous switching is obtained by using a negative differential resistance effect before the forming or setting process in a negative bias. The variations of the low-resistance and high-resistance states in the homogeneous switching are comparable to the filamentary switching cases. However, the drift characteristics of the low-resistance and high-resistance states in the homogeneous switching are unstable with time. Therefore, the short-term plasticity effects, such as the current decay in repeated pulses and paired pulses facilitation, are demonstrated when using the resistance drift characteristics. Finally, the conductance can be increased and decreased by 50 consecutive potentiation pulses and 50 consecutive depression pulses, respectively. The linear conductance update in homogeneous switching is achieved compared to the filamentary switching, which ensures the high pattern-recognition accuracy.

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Section: Introductionmentioning

confidence: 99%

Synaptic Characteristics from Homogeneous Resistive Switching in Pt/Al2O3/TiN Stack

Ryu

Kim

2020

Nanomaterials

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“…Later, the GNMI/CNCCCO electrode was removed from the SC and the FTIR spectrum was measured on its surface, see Figure 7b. It shows new bands at 3100, 1625, 1070 and 945 cm −1 associated with OH‐groups, C=O, C−O−C and CO−OH, [52,53] respectively. The band at 465 cm −1 is ascribed to metal−oxygen bonds.…”

Section: Resultsmentioning

confidence: 99%

Alloy/Perovskite Compounds Modified by Plasma Treatment and their Role in Enhancing the Capacitance of Sustainable Graphene Supercapacitors

Mendoza,

Floriano‐Limón,

Esquivel‐Castro

et al. 2024

ChemistrySelect

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Graphene electrodes were firstly printed on recycled single‐use‐packets. Later, the Ni50Mn35In15 (NMI) alloy or Ca2.9Nd0.1Co3.9Cu0.1O9, (CNCCO) misfit perovskite was deposited on the graphene electrodes to enhance their capacitive performance. Next, flexible supercapacitors (SCs) were assembled by using such electrodes. The SCs made with NMI and CNCCO powders were named as GNMI‐SC, and GCNCCO‐SC, respectively. Those ones produced capacitances/energy‐densities of 761.8 F g−1/152.6 Wh kg−1 and 207.7 F g−1/41.6 Wh kg−1, respectively. Subsequently, a mixture 1 : 1 of NMI and CNCCO powders was melted/coalesced at 1700 °C by using a plasma treatment in argon atmosphere and obtained in this way a NMI/CNCCO composite powder. The SC made with this last composite generated a capacitance/energy‐density of 1235.2 F g−1/247.3 Wh kg−1. Those last values are 62–500 % higher than these for the GNMI‐SC, and GCNCCO‐SC devices. Other benefits obtained after the introduction of the NMI/CNCCO powders into the SCs were: 1) the formation of additional Cu2+, Mn4+, Nd2+ and In3+ species, which enhanced the capacitance; 2) the capacitance retention was maintained above 93 % after 500 cycles of charge discharge; and 3) the lowest values of series and charge transfer resistances were obtained, which favored the ion diffusion/storage in the SC electrodes.

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“…The researchers found that the prepared GO graphene quantum dots as the resistive layer of the device can enhance the performance, such as [185], the device could achieve ternary storage, the ON/OFF current ratio could reach a maximum of 3000, the number of endurance cycles could reach 612, and the device performance significantly increased, [186], the middle resistive layer was GOQDS:PVA, the device exhibited WORM behavior, with ON/OFF current ratio up to 3.3 × 10 4 , and a small set voltage (−0.9 V), Ag/N-GOQDs/Pt devices in [113], which can simulate neural synapses. Some researchers also doped quantum dots into GO-based memory, such as doped graphene quantum dots [106,187], and NC quantum dots [188].…”

Section: Doping With Quantum Dotsmentioning

confidence: 99%

Graphene oxide-based random access memory: from mechanism, optimization to application

Xiu²,

Jiang³

et al. 2022

J. Phys. D: Appl. Phys.

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According to Moore's Law's development law, traditional floating gate memory is constrained by charge tunneling, and its size has approached the physical limit, which is insufficient to meet the requirements of large data storage. The introduction of new information storage devices may be the key to overcoming the bottleneck. Resistive random access memory (RRAM) has garnered interest due to its fast switching speed, low power consumption, and high integration density. Generally, the resistive switching (RS) behaviors can be explained by the formation/rupture of nanoscale conductive filaments (CFs) in many materials, including transition metal oxides, perovskite oxides and organic matter, etc.. Among these materials, graphene oxide (GO) with its unique physical, chemical properties and excellent mechanical properties is attracting significant attention for use in RRAM owing to its RS operation and potential for integration with other graphene-based electronics. The stoichiometry of sp2 to sp3 bonds determines the electrical properties of GO films. The random formation of CFs is usually attributed to the migration of oxygen functional groups driven by electricity field and transition from sp3 to sp2 bonds at the nanoscale. However, there is unacceptable variability in RS reliability, including retention and endurance, which is the key factor that affects the development of memristors. In this article, we discuss systematically several typical models of the switching mechanism of GO-based RRAM and a summary of methods for improving the device's RS performance. This article concludes by discussing the applications of GO-RRAM in artificial neural networks, flexible devices, and biological monitoring.

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Graphene Oxide: Graphene Quantum Dot Nanocomposite for Better Memristic Switching Behaviors

Cited by 14 publications

References 38 publications

Synaptic Characteristics from Homogeneous Resistive Switching in Pt/Al2O3/TiN Stack

Synaptic Characteristics from Homogeneous Resistive Switching in Pt/Al2O3/TiN Stack

Alloy/Perovskite Compounds Modified by Plasma Treatment and their Role in Enhancing the Capacitance of Sustainable Graphene Supercapacitors

Graphene oxide-based random access memory: from mechanism, optimization to application

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