An Artificial Neuron Based on a Threshold Switching Memristor

Zhang, Xumeng; Wang, Wei; Liu, Qi; Zhao, Xiaolong; Wei, Jinsong; Cao, Rongrong; Yao, Zhihong; Zhu, Xiaoli; Zhang, Feng; Lv, Hangbing; Long, Shibing; Liu, Ming

doi:10.1109/led.2017.2782752

Cited by 273 publications

(222 citation statements)

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“…[84,85] A SiO x RRAM device can generate controlled voltage transients, which resemble spike-like responses. [41,96,97] Importantly, these devices relax back to their highly resistive off state spontaneously (without the need of RESET operation) after firing, closely resembling the repolarization process of biological [75] Copyright 2016, Springer Nature. Metal filament-based CBRAM-like devices (diffusive memristors) have also been proved feasible to achieve the integrate-and-fire function.…”

Section: Artificial Neuronsmentioning

confidence: 97%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re‐visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

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Section: Artificial Neuronsmentioning

confidence: 97%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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“…Generally, the amount of discharging in the pulse interval termed as leaky characteristics, while a small amount of discharging in the pulse interval is termed as nonleaky characteristic. [8] We simulated the relationship between the number of spikes and the input current pulse amplitude when the total number of applied pulses was 255, as shown in Figure 4. This indicates that R off determines the leaky/ nonleaky characteristics.…”

Section: Effects Of Ts Device Characteristic On Neuron Functionmentioning

confidence: 99%

“…

To implement a SNN using a hardware system, an integrate and fire (I&F) neuron is commonly adopted as a spiking neuron owing to its simplicity. [6] In this regard, volatile thershold switching (TS) devices [7][8][9][10][11] and nonvolatile memory such as resistive random access memory (RRAM) , [12] phase change random access memory (PRAM), [13] ferromagnetic material, [14] and floating body transistor [15] based I&F neurons have been reported to overcome the limitations of conventional CMOS-based neurons. When the membrane potential reaches the threshold voltage of the neuron, the neuron generates spikes to the next synapse layer and resets the membrane potential.

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mentioning

confidence: 99%

Various Threshold Switching Devices for Integrate and Fire Neuron Applications

Lee

Kwak

Moon

et al. 2019

Adv Elect Materials

109

101

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To implement a SNN using a hardware system, an integrate and fire (I&F) neuron is commonly adopted as a spiking neuron owing to its simplicity. An I&F neuron integrates the input synaptic current and the membrane potential is charged, as shown in Figure 1a. When the membrane potential reaches the threshold voltage of the neuron, the neuron generates spikes to the next synapse layer and resets the membrane potential. Unfortunately, it is becoming burdensome to use conventional CMOS-based neurons in massive neuromorphic hardware due to their large areas and high power consumption. [6] In this regard, volatile thershold switching (TS) devices [7][8][9][10][11] and nonvolatile memory such as resistive random access memory (RRAM) , [12] phase change random access memory (PRAM), [13] ferromagnetic material, [14] and floating body transistor [15] based I&F neurons have been reported to overcome the limitations of conventional CMOS-based neurons. In nonvolatile memory device based I&F neurons wherein the memory device is used for integrating the input synaptic current, an additional circuit is required to return memory device to its initial state in the reset process of the neuron. However, in a TS-based I&F neuron, due to the volatile voltage hysteric switch characteristic of the TS device, a self-reset process is performed without a reset circuit. Thus, it enables the realization of a compact and low power consumption neuron. Although many TS-based I&F neurons have been studied, only the operations of I&F neuron and their biological plausibility have been reported. However, it is necessary to study and understand the correlation between the switching parameter of a TS device and the neuron characteristics for practical application of TS-based I&F neurons in various SNN-based hardware.Therefore, in this work, we investigated the effect of the switching parameters of the TS devices on the characteristics of TS-based I&F neurons through electrical measurements and computational simulation of three different types of neurons using a NbO 2 -based insulator-to-metal transition device (IMT), [16] a B-Te-based ovonic threshold switching (OTS) device, [17] and a Ag/HfO 2 -based atomic-switching TS device. [18] In addition, we confirmed the feasibility of TS-based neuron by simulating SNN, which converted from analog-based ANN prelearned by backpropagation.This study demonstrates an integrate and fire (I&F) neuron using threshold switching (TS) devices to implement spike-based neuromorphic system. An I&F neuron can be realized using the hysteric voltage switch characteristics of a TS device. To investigate the effects of various TS devices on neuron behavior, neurons are compared using three different types of TS device: NbO 2 -based insulator-to-metal transition (IMT) device, B-Te-based ovonic threshold switching device, and Ag/HfO 2 -based atomic-switching TS device. The results show that the off-state resistance and switching time of the TS devices determine the leaky/nonleaky characteristics and types of activation function ...

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“…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

Self Cite

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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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An Artificial Neuron Based on a Threshold Switching Memristor

Cited by 273 publications

References 20 publications

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Various Threshold Switching Devices for Integrate and Fire Neuron Applications

Recent Progress in Synaptic Devices Based on 2D Materials

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