Resistive switching behavior of solution-processed AlOx and GO based RRAM at low temperature

Qi, Yanyuan; Shen, Zongjie; Zhao, Chun; Mitrović, Ivona Z.; Xu, Wangying; Lim, Eng Gee; Yang, Li; He, Jiahuan; Luo, Tian; Huang, Y B; Zhao, Ce Zhou

doi:10.1016/j.sse.2019.107735

Cited by 21 publications

(22 citation statements)

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“…During the fabrication and operation processes of electronic devices, the inherent properties of available materials receive paramount attention [19,32]. As one of the fundamental composition blocks of graphitic materials in all dimensionalities, graphene is a representative flat monolayer of carbon atoms tightly packed into a two-dimensional (2D) honeycomb lattice [33].…”

Section: Properties Of Graphene-based Materialsmentioning

confidence: 99%

“…Hong et al demonstrated a silicon (Si)-based flash memory device with graphene as a floating gate, which presented a wide operation window and low cell-to-cell interference with low operation voltage [45]. Qi and Shen et al reported an RRAM device with a solution-processed GO thin film, which operated with an operation voltage lower than 2 V and a~10 3 on/off ratio [19]. These results suggest the great potential of GRMs in the NVM industry.…”

Section: Properties Of Graphene-based Materialsmentioning

confidence: 99%

“…Following a thermal annealing process, the GO thin film can be fabricated after a suspension deposition process. In general, the annealing temperature is commonly lower than 200 • C, and the thickness range of obtained GO thin films is from about a few tens of nanometers (nm) to a few tens of micrometers (µm) [19,121,[124][125][126][127][128].…”

Section: Rram Based On Graphene and Its Derivativesmentioning

confidence: 99%

“…Several studies reported that the main reason for RS performance in dielectric layers based on graphene and its derivatives, especially GO layers, is the diffusion of oxygen ions or vacancies [19,[134][135][136][137][138][139]. In general, the RS behavior and switching mechanism are influenced by the fabrication methods and hybridization state of graphene-based material dielectric layers, the deposition techniques of electrode layers, and the choice of TE and BE materials.…”

Section: Oxygen Ion-based Switching Mechanismmentioning

confidence: 99%

“…The retention performance indicated that the device could sustain data over 10 4 s, and its endurance cycles exceeded 100. The GO suspension liquid of graphite oxide powder and ethyl alcohol was spin-coated onto the substrate, which indicates the great potential of a solution-processed GO dielectric layer to be deposited onto a flexible substrate [19].…”

Section: Application Of Graphene-based Memristors In Flexible Electromentioning

confidence: 99%

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Memristive Non-Volatile Memory Based on Graphene Materials

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Resistive random access memory (RRAM), which is considered as one of the most promising next-generation non-volatile memory (NVM) devices and a representative of memristor technologies, demonstrated great potential in acting as an artificial synapse in the industry of neuromorphic systems and artificial intelligence (AI), due its advantages such as fast operation speed, low power consumption, and high device density. Graphene and related materials (GRMs), especially graphene oxide (GO), acting as active materials for RRAM devices, are considered as a promising alternative to other materials including metal oxides and perovskite materials. Herein, an overview of GRM-based RRAM devices is provided, with discussion about the properties of GRMs, main operation mechanisms for resistive switching (RS) behavior, figure of merit (FoM) summary, and prospect extension of GRM-based RRAM devices. With excellent physical and chemical advantages like intrinsic Young’s modulus (1.0 TPa), good tensile strength (130 GPa), excellent carrier mobility (2.0 × 105 cm2∙V−1∙s−1), and high thermal (5000 Wm−1∙K−1) and superior electrical conductivity (1.0 × 106 S∙m−1), GRMs can act as electrodes and resistive switching media in RRAM devices. In addition, the GRM-based interface between electrode and dielectric can have an effect on atomic diffusion limitation in dielectric and surface effect suppression. Immense amounts of concrete research indicate that GRMs might play a significant role in promoting the large-scale commercialization possibility of RRAM devices.

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Section: Properties Of Graphene-based Materialsmentioning

confidence: 99%

Section: Properties Of Graphene-based Materialsmentioning

confidence: 99%

Section: Rram Based On Graphene and Its Derivativesmentioning

confidence: 99%

Section: Oxygen Ion-based Switching Mechanismmentioning

confidence: 99%

Section: Application Of Graphene-based Memristors In Flexible Electromentioning

confidence: 99%

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Memristive Non-Volatile Memory Based on Graphene Materials

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Recent Progress in Solution‐Based Metal Oxide Resistive Switching Devices

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Metal oxide resistive switching memories have been a crucial component for the requirements of the Internet of Things, which demands ultra‐low power and high‐density devices with new computing principles, exploiting low cost green products and technologies. Most of the reported resistive switching devices use conventional methods (physical and chemical vapor deposition), which are quite expensive due to their up‐scale production. Solution‐processing methods have been improved, being now a reliable technology that offers many advantages for resistive random‐access memory (RRAM) such as high versatility, large area uniformity, transparency, low‐cost and a simple fabrication of two‐terminal structures. Solution‐based metal oxide RRAM devices are emergent and promising non‐volatile memories for future electronics. In this review, a brief history of non‐volatile memories is highlighted as well as the present status of solution‐based metal oxide resistive random‐access memory (S‐RRAM). Then, a focus on describing the solution synthesis parameters of S‐RRAMs which induce a massive influence in the overall performance of these devices is discussed. Next, a precise analysis is performed on the metal oxide thin film and electrode interface and the recent advances on S‐RRAM that will allow their large‐area manufacturing. Finally, the figures of merit and the main challenges in S‐RRAMs are discussed and future trends are proposed.

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Emerging Optical In‐Memory Computing Sensor Synapses Based on Low‐Dimensional Nanomaterials for Neuromorphic Networks

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Advanced Intelligent Systems

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Emerging optical synapses with in‐memory computing sensor (IMCS) performance are considered to be one of the most effective candidates to circumvent the bottleneck of the current Von Neumann structure while developing neuromorphic systems with higher effectiveness and lower energy consumption. Biomimetic properties of optical IMCS synapses in function and form indicate the higher requirements for utilized functional materials, such as stronger optical sensitivity and lower energy dissipation. Because of properties with high optical‐sensitivity efficiency and excellent electrical conductivity, low‐dimensional nanomaterials have received tremendous interest in modulating optical‐induced synaptic plasticity and emulating optical‐triggered neuromorphic activity of optical IMCS synapses. Herein, a comprehensive summary of optical IMCS synapses based on low‐dimensional nanomaterials is introduced systematically for the first time, including 0D, 1D, and 2D materials. In addition, the content of biomimetic synaptic characteristics, materials classification, operation mechanism, and neuromorphic applications of optical IMCS synapses based on low‐dimensional nanomaterials are also summarized in this work. At last, the challenges and outlook related to artificial optical IMCS synapses with low‐dimensional nanomaterials are provided.

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Resistive switching behavior of solution-processed AlOx and GO based RRAM at low temperature

Cited by 21 publications

References 50 publications

Memristive Non-Volatile Memory Based on Graphene Materials

Memristive Non-Volatile Memory Based on Graphene Materials

Recent Progress in Solution‐Based Metal Oxide Resistive Switching Devices

Emerging Optical In‐Memory Computing Sensor Synapses Based on Low‐Dimensional Nanomaterials for Neuromorphic Networks

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