Emulating Short-Term and Long-Term Plasticity of Bio-Synapse Based on Cu/a-Si/Pt Memristor

Zhang, Xumeng; Liu, Sen; Zhao, Xiaolong; Wu, Facai; Wu, Quantan; Wang, Wei; Cao, Rongrong; Fang, Yilin; Lv, Hangbing; Long, Shibing; Liu, Qi; Liu, Ming

doi:10.1109/led.2017.2722463

Cited by 145 publications

(110 citation statements)

References 16 publications

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“…The difference is that the semiconducting or insulating switching layer does not contain movable metal ions or metal clusters; movable metal ions come from the redox reaction at the active electrode/switching layer interface upon electrical bias. The active electrode material is always Cu or Ag, and the switching layer can be Ta 2 O 5 , ZnO, HfO 2 , WO 3− x , ZnS, GeSe, SiGe, amorphous silicon, amorphous carbon, polymer, and silk fibroin . It has been found that lightly oxidized ZnS films present highly controllable memristive switching and synaptic performance, originating from a two‐layer structure of ZnS thin films, i.e., the lightly oxidized layer and unoxidized layer .…”

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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Section: Working Mechanisms Of Memristive Synapsesmentioning

confidence: 99%

Memristive Synapses for Brain‐Inspired Computing

Wang

Zhuge

2019

Adv Materials Technologies

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“…Similar to the working mechanism of a memristor, the synaptic weight or conductance of the synaptic device varies by the active metal (Ag, Cu, W, etc.) ion migration upon external electrical stimuli, realizing synaptic behaviors, such as potentiation, depression, and STDP . Moreover, the crossbar structure of memristors lends this type of synaptic device promise for high‐density integration.…”

Section: Materials and Heterostructure‐based Synaptic Devicesmentioning

confidence: 99%

“…Various synaptic devices based on resistive switching, driven by different physical working mechanisms such as active metallic filament, charge trapping/detrapping effect, ions/vacancies migration, phase change behaviors, ferroelectric polarization, and spin‐transfer torque‐based synapses, have been demonstrated for emerging memory and neuromorphic computing. Many scientists are actively working to resolve various issues in those synaptic devices: high energy consumption, low switching speed, poor reliability, or the lack of high device density for integration.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Synaptic Devices Based on 2D Materials

Sun

Wang

Yang

2020

Advanced Intelligent Systems

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Diverse synaptic plasticity with a wide range of timescales in biological synapses plays an important role in memory, learning, and various signal processing with exceptionally low power consumption. Emulating biological synaptic functions by electric devices for neuromorphic computation has been considered as a way to overcome the traditional von Neumann architecture in which separated memory and information processing units require high power consumption for their functions. Synaptic devices are expected to conduct complex signal processing such as image classification, decision‐making, and pattern recognition in artificial neural networks. Among various materials and device architectures for synaptic devices, 2D materials and their van der Waals (vdW) heterostructures have been attracting tremendous attention from researchers based on their capacity to mimic unique synaptic plasticity for neuromorphic computing. Herein, the basic operations of biological synapses and physical properties of 2D materials are discussed, and then 2D materials and their vdW heterostructures for advanced synaptic operations with novel working mechanisms are reviewed. In particular, there is a focus on how to design synaptic devices with the vdW structures in terms of critical 2D materials and their limitations, providing insight into the emerging synaptic device systems and artificial neural networks with 2D materials.

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“…Since Jo et al [20] reported artificial synaptic behaviors, such as long-term potentiation/depression (LTP/LTD), in a Si/Ag-based memristor device in 2010, two-terminal memristive devices, such as phase-change random-access memory (PCRAM), [16,21,22] resistive-switching random-access memory (ReRAM), [23][24][25] and conductive bridge random-access memory (CBRAM) devices [26,27] have been studied actively as promising synaptic and neural devices for HNNs. Such devices could gradually change the conductance of the path of current according to input voltage pulses, [16,[21][22][23][24][25][26][27] thereby allowing for the implementation of the functional operation of a synapse within a unit device.…”

mentioning

confidence: 99%

“…[21][22][23][24][25][26][27][28] The extraordinary LTP/LTD profiles with opposite nonlinearity values (negative α p and positive α d ) can be explained by the harsh Cu 2+ diffusion condition in the S-DNA electrolyte owing to the relatively lower number of defective sites. After fitting the data, we confirmed a special potentiation characteristic with a negative nonlinearity value in the S-DNA device.…”

mentioning

confidence: 99%

A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks

Kang

Kim

et al. 2019

Advanced Science

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A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu 2+ ‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu 2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (α p and α d ), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu 2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (|α p |: 31→20, |α d |: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S‐DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy.

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Emulating Short-Term and Long-Term Plasticity of Bio-Synapse Based on Cu/a-Si/Pt Memristor

Cited by 145 publications

References 16 publications

Memristive Synapses for Brain‐Inspired Computing

Memristive Synapses for Brain‐Inspired Computing

Recent Progress in Synaptic Devices Based on 2D Materials

A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks

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