Highly Compact Artificial Memristive Neuron with Low Energy Consumption

Zhang, Yishu; He, Wei; Wu, Yujie; Huang, Kejie; Shen, Yangshu; Su, Jiasheng; Wang, Yaoyuan; Zhang, Ziyang; Ji, Xinglong; Li, Guoqi; Zhang, Hongtao; Song, Sen; Li, Huanglong; Sun, Litao; Zhao, Rong; Shi, Luping

doi:10.1002/smll.201802188

Cited by 94 publications

(79 citation statements)

References 40 publications

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“…It is also different from that seen in Au–ZnO–Au thin film devices where a bipolar nonvolatile resistive switching performance is normally observed . Moreover, this threshold switching behavior is similar to that of a recently developed inorganic diffusive memristor that closely emulated synaptic Ca 2+ dynamics by forming volatile Ag nanoclusters or nanofilaments as well as the ion‐channel induced biomembrane memristive device . In fact, these types of threshold memristive devices can yield more biologically realistic memristor devices and consequently, fully memristive neural networks capable of unsupervised learning…”

Section: Resultssupporting

confidence: 63%

Ultrathin TiO_x Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors

Xiao

Yeow

Nguyễn

et al. 2019

Adv Elect Materials

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standard von Neumann architecture. [1][2][3][4][5] Among the different materials that have been developed, the study of synaptic devices based on 1D semiconductor nanomaterials is still very limited, with notable reports on carbon nanotubes, [6] Bi 1−x Sb x nanowires, [7] TiO 2 nanowires, [8] organic P 3 HT-polyethylene oxide coresheath nanowires, [9] etc. These studies form a solid foundation to the integration and assembly of 1D nanomaterials for large-scale neuromorphic computing application. However, due to their high surface-to-volume ratio, the electrical performance of semiconductor nanowire devices is strongly influenced by the nature of their surface. [10,11] Surface states of semiconductor nanowires, which are closely related to the morphology and sizes of nanowires, naturally exist due to dangling bonds or surface reconstruction of nanowires. Some surface defects or adsorbates can also lead to surface states on the nanowires. These surface states can produce randomly localized charges, which significantly affect the charge transport behavior, potentially causing band-bending between an electrode and the semiconductor nanowire or even pinning of the Fermi level. [10,12] These surface defects can cause variations of the contact behavior (i.e., Ohmic or Schottky), even for the same type of nanowire-electrode configuration. As a typical example, ZnO nanowires, which have been widely used in field effect transistors, [10,13,14] sensors, [15,16] photodetectors, [17,18] mersistors, [19][20][21] etc., have demonstrated contradictory contact behaviors on Au [22][23][24][25] and Ti [26,27] electrodes. Eliminating the effect of surface defects on semiconductor nanowire devices, especially the contact between electrodes and the nanowire is a major obstacle to achieving improved performance in these devices.In this research, we propose the introduction of an ultrathin metal oxide interfacial layer between Au electrodes and ZnO nanowires to minimize the electrical effect of surface defects on ZnO nanowires. An improved and symmetrical volatile threshold memristive behavior [28] is obtained which was further used to mimic some basic shortterm synaptic functionalities such as excitatory current response, paired-pulse facilitation and depression, and short-term plasticity, promising for neuromorphic computing applications. [29][30][31][32][33] One-dimensional semiconductor nanowires have been widely used as important building blocks in a number of devices. However, the performance of these devices is seriously hindered by the surface states/defects on the nanowires, which is a great obstacle to the realization of controllable and predictable characteristics. The introduction of an ultrathin metal oxide layer between Au electrodes and a ZnO nanowire is used to eliminate the surface effects of the nanowires, leading to improved volatile threshold switching performance. Study of the conduction mechanism demonstrates that the TiO x interfacial layer functions as a barrier between the electrodes and the nanowire, wherein th...

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Section: Resultssupporting

confidence: 63%

Ultrathin TiO_x Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors

Xiao

Yeow

Nguyễn

et al. 2019

Adv Elect Materials

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“…Zhang et al reported a LIF neuron model based on single volatile memristor (Pt/FeO x /Ag). [59] The I-V curves are shown in Figure 8d. When the positive sweep voltage surpasses the threshold voltage, the memristor would switch from HRS to LRS abruptly.…”

Section: Volatile Memristor As Artificial Neuronmentioning

confidence: 99%

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Wang

Yang

Mao

et al. 2020

Advanced Intelligent Systems

120

113

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Due to the rapid development of artificial intelligence (AI) and internet of things (IoTs), neuromorphic computing and hardware security are becoming more and more important. The volatile memristors, which feature spontaneous decay of device conductance, own the distinct combination of high similarity to the biological neurons and synapses and unique physical mechanisms. They are excellent candidates for mimicking the synaptic functions and ideal randomness source of the entropy for hardware‐based security. Herein, the recent advances of volatile memristors in devices, mechanisms, and application aspects are summarized. First, a brief introduction is presented to describe the switching type, materials, and temporal response of volatile memristors. Second, the volatile switching mechanisms are discussed and grouped into ion effects, thermal effects, and electrical effects. Third, attention is focused on the applications of volatile memristors for access devices, neuromorphic computing (artificial neurons and synapses), and hardware security (true random number generators and physical unclonable functions). Finally, major challenges and future outlook of volatile memristors for neuromorphic computing and hardware security are discussed.

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“…Bioplausible neurons are simple but prevalent in neuromorphic computing, while biophysical neurons describe some salient features of biological neurons that bioplausible ones fail to capture, such as hyperpolarization. [217][218][219] Hybrid circuits can be subdivided into one comprising a parallel capacitor with a threshold memristive device and another one comprising comparators and memristive devices without threshold. [215] The cores in the implementation of bioplausible neurons refer to realizing LGP and threshold spike process, and two approaches have been developed in regards to switching characteristics of the adopted memristive devices.…”

Section: Memristive Neuronsmentioning

confidence: 99%

“…Thus, it is not surprising to find that some memristive devices work as capacitors in a given time, in particular at HRS state, and a capacitor is included in the equivalent circuits of some memristive devices. In 2018, Zhang et al [217] realized a highly compact LIF neuron with low power consumption using a single threshold-switching device of Pt/FeO x /Ag in a similar way, as shown in Figure 19c,d. In 2017, Pablo and his coworkers [218] reported an LIF neuron based on a single MIT memristive device, as shown in Figure 19.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

154

127

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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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Highly Compact Artificial Memristive Neuron with Low Energy Consumption

Cited by 94 publications

References 40 publications

Ultrathin TiO_x Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors

Ultrathin TiO_x Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Memristive Synapses and Neurons for Bioinspired Computing

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Highly Compact Artificial Memristive Neuron with Low Energy Consumption

Cited by 94 publications

References 40 publications

Ultrathin TiOx Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors

Ultrathin TiOx Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Memristive Synapses and Neurons for Bioinspired Computing

Contact Info

Product

Resources

About

Ultrathin TiO_x Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors

Ultrathin TiO_x Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors