Synapse‐Like Organic Thin Film Memristors

Zhong, Ya‐Nan; Wang, Tian; Gao, Xu; Xu, Jianlong; Wang, Sui‐Dong

doi:10.1002/adfm.201800854

Cited by 159 publications

(134 citation statements)

References 40 publications

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“…A postsynaptic stimulus, which arrives temporarily before presynaptic stimulus (i.e., Δ t < 0), increases synaptic weights. Otherwise, a presynaptic stimulus, which arrives earlier than a postsynaptic stimulus (i.e., Δ t > 0), decreases synaptic weights . The change in synaptic weight (Δ W ) is considered to represent a change in relative conductivity, which is defined as Δ W = ( G t – G 0 )/ G 0 , where G t and G 0 are the conductance before and after the pre and postspiking pairs, respectively .…”

Section: Resultsmentioning

confidence: 99%

A New Memristor with 2D Ti₃C₂T_x MXene Flakes as an Artificial Bio‐Synapse

Yan

Wang

Zhao

et al. 2019

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159

106

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Two‐dimensional (2D) materials have attracted extensive research interest in academia due to their excellent electrochemical properties and broad application prospects. Among them, 2D transition metal carbides (Ti3C2Tx) show semiconductor characteristics and are studied widely. However, there are few academic reports on the use of 2D MXene materials as memristors. In this work, reported is a memristor based on MXene Ti3C2Tx flakes. After electroforming, Al/Ti3C2Tx/Pt devices exhibit repeatable resistive switching (RS) behavior. More interestingly, the resistance of this device can be continuously modulated under the pulse sequence with 10 ns pulse width, and the pulse width of 10 ns is much lower than that in other reported work. Moreover, on the nanosecond scale, the transition from short‐term plasticity to long‐term plasticity is achieved. These two properties indicate that this device is favorable for ultrafast biological synapse applications and high‐efficiency training of neural networks. Through the exploration of the microstructure, Ti vacancies and partial oxidation are proposed as the origins of the physical mechanism of RS behavior. This work reveals that 2D MXene Ti3C2Tx flakes have excellent potential for use in memristor devices, which may open the door for more functions and applications.

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Section: Resultsmentioning

confidence: 99%

A New Memristor with 2D Ti₃C₂T_x MXene Flakes as an Artificial Bio‐Synapse

Yan

Wang

Zhao

et al. 2019

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159

106

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“…[5,27] In case of ECM mechanism, the variation of the internal resistance of the device is a consequence of the electromigration of metal cations through a solid electrolyte, while in VCM system resistive switching is triggered by the migration of oxygen anions (and is commonly described by the motion of the corresponding oxygen vacancies). [29] Despite memristive behavior was observed in a wide range of materials, such as amorphous and polycrystalline transition metal-oxide, [24][25][26] perovskite oxides and chalcogenides, [24,30] 2D materials-based structures, [31][32][33][34][35] and organic materials; [36,37] the resistive switching mechanisms are far from being completely understood and dominated. [29] Despite memristive behavior was observed in a wide range of materials, such as amorphous and polycrystalline transition metal-oxide, [24][25][26] perovskite oxides and chalcogenides, [24,30] 2D materials-based structures, [31][32][33][34][35] and organic materials; [36,37] the resistive switching mechanisms are far from being completely understood and dominated.…”

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confidence: 99%

Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires

Milano

Porro

Valov

et al. 2019

Adv Elect Materials

101

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Memristive devices are considered one of the most promising candidates to overcome technological limitations for realizing next‐generation memories, logic applications, and neuromorphic systems in the modern nanoelectronics and information technology. Despite the continuous efforts, understanding the resistive switching mechanism underlying memristive/neuromorphic behavior still represents a challenge. Metal oxide nanowire‐based memristors appear suitable model systems for a deeper understanding of the involved physical/chemical phenomena due the possibility for localizing and investigating the switching mechanism. In practical aspects, nanowire‐based devices can be grown using a bottom‐up approach, thus being considered reliable candidates for going beyond the current scaling limitations of the top‐down approach by the standard lithography. In addition, taking into advantage of the high surface‐to‐volume ratio of these nanostructures, new device functionalities can be achieved by exploiting surface effects. In the literature, a variety of nanowire‐based devices such as single nanowires, nanowire arrays, and networks are reported to exhibit memristive behavior, explained by different switching mechanisms. This work provides a comparative review and a comprehensive analysis of device performances and physical phenomena responsible for memristive and neuromorphic behavior in such nanostructures. The analysis of the state‐of‐art of memristor devices based on nanowires and nanorods represent a milestone toward the development of nanowire‐based artificial neural networks.

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“…Numerous synaptic plasticity with different time course have been demonstrated in various memristive systems based on oxides, [182,183] organics, [184,185] biomaterials, [186,187] etc. Numerous synaptic plasticity with different time course have been demonstrated in various memristive systems based on oxides, [182,183] organics, [184,185] biomaterials, [186,187] etc.…”

Section: Homosynaptic Plasticitymentioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

154

126

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and constant data movement are needed while working. In the human brain, huge and complex neural networks composed of gigantic amounts of neurons massively interconnected by an even larger number of synapses are in charge of computing and memory. Unlike a digital computer, information storage and processing happen at the same time in the brain. [3] Artificial intelligence (AI) that could rival biological neural networks is being continuously pursued by human being. There are two approaches to implement AI: one is the software simulation of neural networks with the aid of digital computers, another is developing hardware-integrated circuits of neural networks. In fact, AI based on the software simulation is evolving very fast thanks to the advances in the development of algorithm in recent years, and it even outperforms the human brain in specific complex tasks, such as playing the game of Go. [4] However, software-based AI suffers from critical issues of enormous power consumption and massive data throughput. Hardware-based AI systems are more efficient in terms of speed and power consumption; however, complementary metal oxide semiconductor (CMOS) transistors are inherently different from the basic building blocks of biological neural networks, neurons, and synapses, in terms of operation dynamic and behavior. A circuit consisting of at least ten transistors are required to realize the function of one biological synapse, which is impractical for the implementation of large networks, not to mention the human brain (roughly 10 11 neurons interconnected by ≈10 15 synapses). [5,6] Fortunately, the emergence of memristive devices with innate dynamics resembling biological synapses and neurons provides a feasible and simple way to build hardware-based bio-inspired computing systems. [7,8] For instance, only one compact memristive device with a metalinsulator-metal (MIM) sandwich structure is sufficient to reproduce some functions of a biological synapse. Moreover, the vector-matrix multiplication, which is believed to be the most data-intensive work in neural networks, can be naturally performed in a cross-bar array of memristive devices on the physical level based on Ohm and Kirchhoff laws. [9,10] These features endow the memristive devices ideally suited to realize highly efficient bioinspired computing systems in hardware.To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state-of-art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biore...

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Synapse‐Like Organic Thin Film Memristors

Cited by 159 publications

References 40 publications

A New Memristor with 2D Ti₃C₂T_x MXene Flakes as an Artificial Bio‐Synapse

A New Memristor with 2D Ti₃C₂T_x MXene Flakes as an Artificial Bio‐Synapse

Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires

Memristive Synapses and Neurons for Bioinspired Computing

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Synapse‐Like Organic Thin Film Memristors

Cited by 159 publications

References 40 publications

A New Memristor with 2D Ti3C2Tx MXene Flakes as an Artificial Bio‐Synapse

A New Memristor with 2D Ti3C2Tx MXene Flakes as an Artificial Bio‐Synapse

Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires

Memristive Synapses and Neurons for Bioinspired Computing

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Product

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A New Memristor with 2D Ti₃C₂T_x MXene Flakes as an Artificial Bio‐Synapse

A New Memristor with 2D Ti₃C₂T_x MXene Flakes as an Artificial Bio‐Synapse