Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots

Wang, Changhong; He, Wei; Tong, Yi; Zhang, Yishu; Huang, Kejie; Li, Song; Zhong, Shuangling; Ganeshkumar, Rajasekaran; Zhao, Rong

doi:10.1002/smll.201603435

Cited by 47 publications

(43 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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

View full text Add to dashboard Cite

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...

show abstract

Section: Working Mechanisms Of Memristive Synapsesmentioning

confidence: 99%

Memristive Synapses for Brain‐Inspired Computing

Wang

Zhuge

2019

Adv Materials Technologies

View full text Add to dashboard Cite

show abstract

“…In particular, high yield and low variability are typically required to maximize the computing accuracy of offline training memristive systems, [179,231] and the nonlinearity and asymmetry between increasing/decreasing responses limit the maximum possible accuracy in online training networks. At the device level, the major obstacle is immature memristive device technology, which has a serious effect on the yield, stability, and uniformity of device arrays.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

177

127

View full text Add to dashboard Cite

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...

show abstract

“…Unfortunately, the widely used conductive filament memristors' uncontrollable working mechanism has made this issue more severe and hard to be fixed because of the two-terminal nature. 18,31 Using the gate tuning function, the device variation could be compensated by applying an extra adjusting voltage on the gate terminal. As a proof-of-concept, Fig.…”

Section: Memristive Featuresmentioning

confidence: 99%

Tunable flexible artificial synapses: a new path toward a wearable electronic system

et al. 2018

View full text Add to dashboard Cite

The flexible electronics has been deemed to be a promising approach to the wearable electronic systems. However, the mismatching between the existing flexible deices and the conventional computing paradigm results an impasse in this field. In this work, a new way to access to this goal is proposed by combining flexible devices and the neuromorphic architecture together. To achieve that, a high-performance flexible artificial synapse is created based on a carefully designed and optimized memristive transistor. The device exhibits high-performance which has near-linear non-volatile resistance change under 10,000 identical pulse signals within the 515% dynamic range, and has the energy consumption as low as 45 fJ per pulse. It also displays multiple synaptic plasticity features, which demonstrates its potential for real-time online learning. Besides, the adaptability by virtue of its threeterminal structure specifically contributes its improved uniformity, repeatability, and reduced power consumption. This work offers a very viable solution for the future wearable computing.

show abstract

Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots

Cited by 47 publications

References 50 publications

Memristive Synapses for Brain‐Inspired Computing

Memristive Synapses for Brain‐Inspired Computing

Memristive Synapses and Neurons for Bioinspired Computing

Tunable flexible artificial synapses: a new path toward a wearable electronic system

Contact Info

Product

Resources

About