Dual-Tunable Memristor Based on Carbon Nanotubes and Graphene Quantum Dots

Wang, Lu; Yang, Jing; Zhang, Yukai; Wen, Dianzhong

doi:10.3390/nano11082043

Cited by 16 publications

(8 citation statements)

References 37 publications

(49 reference statements)

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“…In comparison with other synthetic routes, most CDs obtained via electrochemical synthesis display distinct multi‐layer nanostructures. [ 23 ] Additionally, CDs can be easily tailored into different sizes during the synthetic process, which can be applied in different scenarios. [ 24 ]…”

Section: Preparation Of Cds Materialsmentioning

confidence: 99%

“…Wang et al combined multi-walled carbon nanotubes (MWCNTs) with GQDs to fabricate Al/MWCNT@GQDs/ITO memristor. [23] Due to the high UV sensitivity and tunable resistance property of MWCNT@GQDs, the memristive behavior can be realized by variable voltage stimulus and illumination duration. The Al/MWCNT@GQDs/ITO memristor presented programmed and erased dual-tunable performance with improved reproducibility.…”

Section: Cds On Organic Substancesmentioning

confidence: 99%

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Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System

et al. 2023

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In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs-based memristors, and their state-of-the-art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high-quality CDs with desired properties. Then, the structure-property relationship and resistive switching mechanism of CDs-based memristors are discussed in depth. The current challenges and prospects of memristor-based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs-based memristors, including neuromorphic sensors and vision, low-energy quantum computation, and human-machine collaboration.

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Section: Preparation Of Cds Materialsmentioning

confidence: 99%

Section: Cds On Organic Substancesmentioning

confidence: 99%

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System

et al. 2023

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Section: The Modulation Effects Of Cds On Memristancementioning

confidence: 99%

“…also found that UV light has an essential effect for GQDs on modulating memristance through studying the memristor (Figure 14e) based on the composite consisting of GQDs and multiwalled carbon nanotubes (MWCNTs). [ 108 ] The maximum switching current ratio fell off sharply from 4.58 × 10 3 to 10.77 with the illumination time increasing to 30 min (Figure 14f). The reason may be ascribed to the decrease of oxygen‐containing groups and the increase of sp 2 carbon atoms, reducing the trap concentration and switching current ratio.…”

Section: The Modulation Effects Of Cds On Memristancementioning

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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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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Printed Memristors: An Overview of Ink, Materials, Deposition Techniques, and Applications

Franco,

Kiazadeh,

Martins

et al. 2024

Adv Elect Materials

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Industry 4.0 is accelerating the growth of connected devices, resulting in an exponential increase in generated data. The current semiconductor technology is facing challenges in miniaturization and power consumption, demanding for more efficient computation where new materials and devices need to be implemented. One of the most promising candidates for the next technological leap is the memristor. Due to their up‐scale manufacturing, the majority of memristors employed conventional deposition techniques (physical and chemical vapor deposition), which can be highly costly. Recently, printed memristors have gained a lot of attention because of their potential for large‐scale, fast, and affordable manufacturing. They can also help to reduce material waste, which supports the transition to a more sustainable and environmentally friendly economy. This review provides a perspective on the potential of printed electronics in the fabrication of memristive devices, presenting an overview of the main printing techniques, most suitable for memristors development. Additionally, it focuses on the materials used for the switching layer by comparing its performance. Ultimately, the application of printed memristors is highlighted by showing the tremendous evolution in this field, as well as the main challenges and opportunities that printed memristors are expected to face in the following years.

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Dual-Tunable Memristor Based on Carbon Nanotubes and Graphene Quantum Dots

Cited by 16 publications

References 37 publications

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System

Research Process of Carbon Dots in Memristors

Printed Memristors: An Overview of Ink, Materials, Deposition Techniques, and Applications

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