Top‐Down Synthesis of Hollow Graphene Nanostructures for Use in Resistive Switching Memory Devices

Anoop, Gopinathan; Kim, Tae Yeon; Lee, Hye Jeong; Panwar, Varij; Kwak, Jin Ho; Heo, Yooun; Yang, Jin-Hoon; Lee, Joo Hyoung; Jo, Ji Young

doi:10.1002/aelm.201700264

Cited by 8 publications

(6 citation statements)

References 70 publications

(140 reference statements)

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“…[45,46] In the Ohmic region, current is proportional to electric field; in the Child's region, the injected electrons are trapped to form the space charge effect and drift current; in the TCLC region, the traps are gradually filled by electrons with the increasing electric field, then the concentration of free electrons increases sharply, and the device enters LRS. [45][46][47][48][49] iv) Fowler-Nordheim (F-N) tunneling (ln(I/V 2 ) ∝ 1/V), a quantum effect that electrons directly pass through the triangular barrier at the interface between the metal and functional layer. [50,51] v) Poole-Frenkel (P-F) emission (ln(I/V) ∝ V 1/2 ), in which the electrons in traps are excited into the conduction band.…”

Section: Classification Of Memristorsmentioning

confidence: 99%

“…As hollow GQDs, graphene nanorings (GNRs) in the Pt/ GNRs-P4VP/ITO/PET memristor modulate memristance by electron trapping and detrapping effect. [46] Typical bipolar resistive switching behavior shows a switching current ratio of 2 × 10 4 in Figure 9a. The electron transport mode of HRS is in keeping with the classic SCLC (Figure 9b).…”

Section: Electron Trapping and Detrapping Effect Of Cdsmentioning

confidence: 99%

“…e) SET and RESET voltage distributions for 100 cycles.f-i) Schematic of energy band and electron transport process in unbiased state (f), on trapping process (g), during SET process (h), and on RESET process (i). Reproduced with permission [46]. Copyright 2017, Wiley-VCH.…”

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Research Process of Carbon Dots in Memristors

Hao

Yan

Wang

et al. 2023

Adv Elect Materials

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The number of interconnected equipment is predicted to surge to 75 billion by 2025, and the involved global data will swell to 175 zettabytes. [3,4] The massive data threaten the prevailing von Neumann architecture, whose processing and memory units are physically separated, resulting in low efficiency when handling artificial intelligence (AI) tasks. [5] Process technologies also restrict the development of processors and memorizers along the path of Moore's law, seriously restricting hardware computational power and storage capacity. Although complementary metal-oxide-semiconductor (CMOS) has played a crucial role in modern civilization, it is difficult to satisfy the demand of Big Data and AI era. [6] Therefore, it is inevitable to develop a new chip architecture and device technology. The inspiration for new technology comes from the neural network that is constituted of 10 11 neurons and 10 15 synapses. [7] On this basis, the human brain can not only learn and memorize information autonomously, but also recognize, reason, and process multiple tasks simultaneously, which features low energy consumption, high stability, and parallel processing. Therefore, the computing and memory devices mimicking brain have drawn extensive attention in recent years. [8,9] Among these emerging devices, memristors are expected to realize neuromorphic processors characterizing in situ in-memory computations using networks-on-chip in an event-driven manner. [10] By virtue of the history of the amount of flowing charges, memristors can be applied to artificial neural network (ANN) and data memory. [11][12][13] Flexible memristors can satisfy the requirements of portable, wearable, and implantable applications. [14] The controllability of memristors can ensure data-processing accuracy and stability. Specifically, the memristance modulation can manifest as the mollification of spatial (device-to-device) and temporal (cycle-to-cycle) variability or the realization of interconversions between different resistive switching behaviors. [15,16] The classical memristor consists of top and bottom electrodes and an intermediate functional layer (Figure 1a), which is an easy integration with CMOS and highly extensible two-terminal device. [17][18][19][20] Functional layer is the heart of memristor, and the related materials determine its performance. Since the first physical model of memristor was reported using TiO 2 as a functional layer, many materials, including carbon dots (CDs), have exhibited good memristive performances. [21][22][23] As a kind of carbon material, CDs generally possess remarkable characteristics, including physicochemical stability, The explosive growth of digital communication promotes advanced memory and computing devices in Big Data and artificial intelligence era. In particular, memristors hold great promise for in-memory computing and artificial synapses, expected to break through restrictions on hardware computational power and storage capacity caused by the von Neumann bottleneck and declining Moore's Law. The m...

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Section: Classification Of Memristorsmentioning

confidence: 99%

Section: Electron Trapping and Detrapping Effect Of Cdsmentioning

confidence: 99%

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Research Process of Carbon Dots in Memristors

Hao

Yan

Wang

et al. 2023

Adv Elect Materials

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“…材料制备过程中的缺陷、掺杂与空位等因素都会影响其阻变行为. 研究者提出了许多阻变效应的作用机理, 如导电细丝形成/断裂 [12,27,28] 、陷阱填充限制电流 [13,29,30] 与界面肖特基势垒调制 [18,31,32] 等, 得到了广泛认可; 而量子点材料的阻变机理较为复电细丝/形成断裂机理的基本特征 [33,34] ; 同时I-V 曲线中并未出现界面肖特基势垒调制机理常有的正负向偏压 LRS电流不对称现象 [35,36] , 这表明 SnO 2 QDs的阻变效应并非由单一的阻变机理控制.…”

Section: 阻变机理及调控机制研究unclassified

Size-controlled resistive switching performance and regulation mechanism of SnO2 QDs

Gong¹,

Zhou²,

Wang³

et al. 2021

Acta Phys. Sin.

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As a non-volatile memory, zero-dimensional quantum dot resistive random access memory (RRAM) has shown broad application prospects in the field of intelligent electronic devices due to its advantages of simple structure, low switching voltage, fast response speed, high storage density, and low power consumption. Tin dioxide quantum dots (SnO2 QDs) are a good option for resistive functional materials with excellent physical and chemical stabilities, high electron mobilities, and adjustable energy band structures. In this paper, the SnO2 QDs with sizes of 2.51 nm, 2.96 nm and 3.53 nm are prepared by the solvothermal method, and the quantum size effect is observed in a small size range and the effective regulation of resistive switching voltage is achieved based on its quantum size effect, which is the unique advantage of quantum dot material in comparison with that of bulk material. Research result shows that as the size of SnO2 QD increases, the SET/RESET voltage gradually decreases from –3.18 V/4.35 V to –2.02 V/3.08 V. The 3.53 nm SnO2 QDs have lower SET/RESET voltage (–2.02 V/3.08 V) and larger resistive switching ratio (> 104), and the resistive switching performance of the device has changed less than 5% after having experienced durability tests 2 × 104 times, showing good stability and retention. Besides, according to the fitting of charge transport mechanism, SnO2 QD RRAM exhibits Ohmic conduction under LRS, while Ohmic conduction, thermionic emission and space charge limit current work together during HRS. The resistive switching effect of SnO2 QDs is controlled by trap filled limit current and interface Schottky Barrier modulation; the trapping/de-trapping behavior of internal defect potential well of SnO2 QDs on electrons dominates the HRS/LRS switching, while the effective control of ITO/SnO2 QDs and SnO2 QDs/Au interface Schottky barrier is the key to accurately regulating the switching voltage. The reason why SnO2 QD RRAM exhibits good size-switching voltage dependence is that the larger SnO2 QD has lower Fermi level and interface Schottky barrier height, so the junction resistance voltage division is reduced, and the SET/RESET voltage decrease accordingly. This work reveals the huge application potential and commercial application value of SnO2 QDs in the field of resistive switching memory, and provides a new option for the development of RRAM.

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“…Meanwhile, graphene‐based materials have been extensively studied in the RRAM field because of their excellent mechanical and electrical properties . Recently, a zinc oxide–graphene hybrid was prepared to harness the unique properties of these two components for novel applications .…”

mentioning

confidence: 99%

Improved Resistive Memory Based on ZnO–Graphene Hybrids through Redox Process of Graphene Quantum Dots

Chen

Gao

Chen

et al. 2019

Physica Rapid Research Ltrs

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Zinc oxide–graphene quantum dot (ZnO–GQD) hybrid thin films are synthesized by solution processing. Memory devices with Al/ZnO–GQD/Pt structures exhibit reliable bipolar switching performances with an excellent uniform distribution of switching parameters, ultra‐low switching voltages, and high ON/OFF ratios. Compared with a pure ZnO‐based device, the switching voltages of the Al/ZnO–GQD/Pt device are decreased by 75%, and its coefficient of variation is one order of magnitude lower than that of the pure ZnO‐based device. X‐ray photoelectron spectroscopy and Raman spectroscopy analyses reveal the reversible redox of GQDs under an applied electric field, which facilitates the formation and rupture of conductive filaments. The reversible exchange of oxygen ions between the GQDs and ZnO under an electric field guides the practical application of such hybrid materials in high‐performance nonvolatile memory devices.

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Top‐Down Synthesis of Hollow Graphene Nanostructures for Use in Resistive Switching Memory Devices

Cited by 8 publications

References 70 publications

Research Process of Carbon Dots in Memristors

Research Process of Carbon Dots in Memristors

Size-controlled resistive switching performance and regulation mechanism of SnO<sub>2</sub> QDs

Improved Resistive Memory Based on ZnO–Graphene Hybrids through Redox Process of Graphene Quantum Dots

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