An accurate model of domain-wall-based spintronic memristor

Nafea, Sherif F.; Dessouki, Ahmed A. S.; El-Rabaie, El-Sayed M.; Elnaghi, Basem E.; Ismail, Yehea; Mostafa, Hassan

doi:10.1016/j.vlsi.2018.12.001

Cited by 13 publications

(5 citation statements)

References 27 publications

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“…While in most cases combined with nonmagnetic materials, typical magnetic structures like domain walls or skyrmions, as described in this paper, form a broad base for neuromorphic hardware elements with significantly different properties, which are important for their possible applications, as Table 2 shows. Endurance High [83] High [84] High [85] High [86] High [87] Programming accuracy High [88] Low, algorithmic scaling is necessary [88] Can be high [89] High [86] Can be sufficiently controlled [90] Power consumption Low [13,83,91] Very low (~100 nW) possible [83] Low [92] Low [83] Low [93] Speed High (few ns) [13,83] High (~100 ns) [88] High [92] Low (e.g., <100 kHz) [93] High (~10 ns) [90] Area consumption Relatively high [13] High due to high synaptic density [88] Low [92] Low, depends on technique [94,95] Low [87] Retention <10 years [91] or more with sophisticated concepts [96]~1 0 years with a sophisticated concept [96] High [85]~10 years [86] Enabling shortand long-term memory [97] Scalability Possible [13] Possible, but challenging [88] Improvable based on recent findings [85] Possible [98] Good…”

Section: Discussionmentioning

confidence: 99%

Magnetic Elements for Neuromorphic Computing

Błachowicz

Ehrmann

2020

Molecules

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Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck. Artificial synapses and neurons can be implemented into conventional hardware using new software, but also be created by diverse spintronic devices and other elements to completely avoid the disadvantages of recent hardware architecture. Here, we report on diverse approaches to implement neuromorphic functionalities in novel hardware using magnetic elements, published during the last years. Magnetic elements play an important role in neuromorphic computing. While other approaches, such as optical and conductive elements, are also under investigation in many groups, magnetic nanostructures and generally magnetic materials offer large advantages, especially in terms of data storage, but they can also unambiguously be used for data transport, e.g., by propagation of skyrmions or domain walls. This review underlines the possible applications of magnetic materials and nanostructures in neuromorphic systems.

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Section: Discussionmentioning

confidence: 99%

Magnetic Elements for Neuromorphic Computing

Błachowicz

Ehrmann

2020

Molecules

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“…For example, the resistance change rate of spintronic memristors is relatively fast, which greatly enhances the data storage function. In addition, the anti-interference ability of the spintronic memristor is relatively strong. ,, Spintronic memristors combine nonvolatility with the scalability of STT devices, both of which indicate the prospect of high-performance and high-density memory technology . Besides, as presented in Figure , spintronic memristors are promising devices in the neuromorphic fields, , memory chips, , and temperature sensors …”

Section: Introductionmentioning

confidence: 98%

“…36,62,63 Spintronic memristors combine nonvolatility with the scalability of STT devices, both of which indicate the prospect of highperformance and high-density memory technology. 64 Besides, as presented in Figure 4, spintronic memristors are promising devices in the neuromorphic fields, 65,66 memory chips, 67,68 and temperature sensors. Traditional electronic devices are usually based on nonmagnetic semiconductors and realize some circuit functions by controlling the charge flow.…”

Section: Introductionmentioning

confidence: 99%

From Spintronic Memristors to Quantum Computing

Qin

Sun

Zhou

et al. 2023

ACS Materials Lett.

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The high-speed development of the Internet of Things and artificial intelligence is revolutionizing the world in terms of industrial production, environmental protection, medical treatment, education, daily life, and so on. The powerful and fast computing methods are crucial for the advanced computing technology toward the next generation artificial intelligence. Traditional computing systems have separated logical and storage units, which cause computation time delays and increase power consumption. Spintronic memristors combine the nonvolatile characteristics of memristors with the scalability of a spin-transfer torque device, which can meet the high-speed, low-power, and scalability requirements of quantum computing (QC) for quantitative information processing. This paper reviews the research progress of spintronic memristors based on magnetic tunnel junction (MTJ), domain wall (DW) motion, and spin wave (SW), respectively, focusing on the development and challenges of spintronic memristors for QC. Finally, some problems that need to be solved urgently in the current research are summarized, and the potential applications of spintronic memristors are discussed.

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“…One of the applications that TiO2 proved to be a significant candidate was the "memristor" device [12,13]. The memristor or memory resistor is a device that remembers data in terms of electrical device resistance [14][15][16]. Recently, many materials have been used to prepare the memristors, including metal oxides, carbon-based materials, and organic materials [17].…”

Section: Introductionmentioning

confidence: 99%

Influence of Phase Structure of TiO2 Nanoparticles on Resistive Switching Devices

Tunhoo,

Chantarawong,

Thiwawong

et al. 2023

Curr. Appl. Sci. Technol.

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In this work, the electrical memory properties of a bi-stable device based on the structure of aluminum/poly (9-vinyl carbazole) (PVK): TiO2 NPs/indium-tin-oxide (ITO) were reported. The effects of the modified phase structural of titanium dioxide nanoparticles (TiO2 NPs) on the electrical memory characteristics were determined. The TiO2 NPs were annealed at different annealing temperatures. The physical properties of the annealed TiO2 NPs were characterized by transmission electron microscope and X-ray diffraction, which revealed the composition and structure of the anatase and rutile phases. Current-voltage measurements showed that the bi-stable characteristics were affected by the phase structure of the TiO2 NPs. The ON/OFF current ratio of the fabricated device was noted to be approximately 2.06×105 in the case of TiO2 NPs annealed at 500°C. A theoretical model was used to explain the charge injection mechanisms of the device. Moreover, the temperature dependence and retention-time measurements of the device were demonstrated.

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An accurate model of domain-wall-based spintronic memristor

Cited by 13 publications

References 27 publications

Magnetic Elements for Neuromorphic Computing

Magnetic Elements for Neuromorphic Computing

From Spintronic Memristors to Quantum Computing

Influence of Phase Structure of TiO2 Nanoparticles on Resistive Switching Devices

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