Flexible memristors as electronic synapses for neuro-inspired computation based on scotch tape-exfoliated mica substrates

Yan, Xiaobing; Zhou, Zhenyu; Zhao, Jianhui; Liu, Qi; Wang, Hong; Yuan, Guoliang; Chen, Jingsheng

doi:10.1007/s12274-017-1781-2

Cited by 98 publications

(65 citation statements)

References 54 publications

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“…In paired‐pulse facilitation (PPF), an important short‐term synaptic plasticity in neuroscience research, the promotion of output signals is determined by the time interval between a pair of input stimuli. In biological systems, PPF occurs when a second pulse in the postsynaptic current is enhanced . Pulses with amplitudes of 4 and −4 V are applied to the device, and the pulse waveform of PPF is shown in Figure S10 in the Supporting Information.…”

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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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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108

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“…However, no obvious advantage can be found over synaptic devices based on bilayer and trilayer structures. Due to a simple structure, oxide single‐layers have also been widely used for synaptic devices, including AlO x , FeO x , HfO x , PrCaMnO x , SrTiO 3 , TaO x , TiO x , WO x , ZnHfO x , ZrHfO x , KNbO 3 , BiFeO 3 , SiO x , and NiO x . The working mechanism of PrCaMnO x ‐based memristive devices is widely attributed to field‐driven oxygen migration and redox reaction at a metal/PrCaMnO x interface .…”

Section: Working Mechanisms Of Memristive Synapsesmentioning

confidence: 99%

Memristive Synapses for Brain‐Inspired Computing

Wang

Zhuge

2019

Adv Materials Technologies

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be precisely regulated by the ionic flow, which is called synaptic plasticity. Generally, there are two types of synaptic plasticity: potentiation and depression. In the case of potentiation or depression, the synaptic weight increases or decreases upon repeated stimulation. Inspired by the human brain, novel non von Neumann computing architectures mainly composed of artificial synapses and artificial neurons have been proposed, which we can call "artificial neural networks," "brain-like computers," or "neuromorphic computers." [1,[3][4][5] However, the development of artificial brains that possess the efficiency of their biological counterparts in information processing remains a major challenge in computing. [6] One of the major challenges is the lack of suitable hardware to implement synapses, which are the main data processing elements in artificial brains. Generally, there are four essential requirements for an artificial synapse. [7,8] First, the analog weight should be nonvolatile in the absence of learning and weight updates should be bidirectional when the synapse is learning. Second, the synapse output is the product of the input signal and the synapse weight. Third, the weight updates vary with both the input signal and the stored weight value. Fourth, the synapse should be rather compact, and operate off a unidirectional information transfer with low power consumption.Mead and co-workers fabricated a four-terminal synaptic device based on a single floating gate silicon transistor in 1995. [8] It can simultaneously perform long term weight storage, compute the product of the input and floating gate value, and update the weight value according to a Hebbian or a backpropagation learning rule. In 1996, they developed a new floating-gate silicon transistor synapse with a threeterminal structure. [7] Hot-electron injection and electron tunneling make possible bidirectional weight updates. Given that these updates depend on both the stored weight value and the transistor terminal voltages, such synapse can implement a learning function. However, the integration density of transistor synapses is severely restricted to their three-or fourterminal structures. [6] The emergence of memristors endows artificial synapses with a new development opportunity. The memristor (short for memory resistor) is a two-terminal circuit element characterized by a relationship between the charge and the flux-linkage, which shows a distinctive "fingerprint" characterized by a pinched hysteresis loop confined to the first and the third quadrants of the Although the structure and function of the human brain are still far from being fully understood, brain-inspired computing architectures mainly consisting of artificial neurons and artificial synapses have been attracting more and more attentions due to their powerful computing capability and energy efficient operation. Synaptic plasticity is believed to be the origin of learning and memory. However, it is still a big challenge to realize artificial synapses with high reliability, go...

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“…STDP is among the most important Hebbian learning rules for learning and memory . In STDP, the relative timing of pre‐ and postsynaptic spikes and the time interval (Δ t ) between them determine the changes in synaptic weights . More specifically, if the presynaptic spike arrives before (after) the postsynaptic spike (pre–post/post–pre), the pairs will cause potentiation (depression), and a larger Δ t will lead to a smaller weight modification.…”

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confidence: 99%

Vacancy‐Induced Synaptic Behavior in 2D WS₂ Nanosheet–Based Memristor for Low‐Power Neuromorphic Computing

Yan

Zhao

Chen

et al. 2019

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Memristors with nonvolatile memory characteristics have been expected to open a new era for neuromorphic computing and digital logic. However, existing memristor devices based on oxygen vacancy or metal‐ion conductive filament mechanisms generally have large operating currents, which are difficult to meet low‐power consumption requirements. Therefore, it is very necessary to develop new materials to realize memristor devices that are different from the mechanisms of oxygen vacancy or metal‐ion conductive filaments to realize low‐power operation. Herein, high‐performance and low‐power consumption memristors based on 2D WS2 with 2H phase are demonstrated, which show fast ON (OFF) switching times of 13 ns (14 ns), low program current of 1 µA in the ON state, and SET (RESET) energy reaching the level of femtojoules. Moreover, the memristor can mimic basic biological synaptic functions. Importantly, it is proposed that the generation of sulfur and tungsten vacancies and electron hopping between vacancies are dominantly responsible for the resistance switching performance. Density functional theory calculations show that the defect states formed by sulfur and tungsten vacancies are at deep levels, which prevent charge leakage and facilitate the realization of low‐power consumption for neuromorphic computing application.

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Flexible memristors as electronic synapses for neuro-inspired computation based on scotch tape-exfoliated mica substrates

Cited by 98 publications

References 54 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

Memristive Synapses for Brain‐Inspired Computing

Vacancy‐Induced Synaptic Behavior in 2D WS₂ Nanosheet–Based Memristor for Low‐Power Neuromorphic Computing

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Flexible memristors as electronic synapses for neuro-inspired computation based on scotch tape-exfoliated mica substrates

Cited by 98 publications

References 54 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

Memristive Synapses for Brain‐Inspired Computing

Vacancy‐Induced Synaptic Behavior in 2D WS2 Nanosheet–Based Memristor for Low‐Power Neuromorphic Computing

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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

Vacancy‐Induced Synaptic Behavior in 2D WS₂ Nanosheet–Based Memristor for Low‐Power Neuromorphic Computing