A Compact Artificial Spiking Neuron Using a Sharp-Switching FET With Ultra-Low Energy Consumption Down to 0.45 fJ/Spike

Chen, Yingxin; Xiao, Kai; Qin, Yajie; Liu, Fanyu; Wan, Jing

doi:10.1109/led.2022.3219465

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…[8] Nevertheless, many neural devices used for SNNs are the mainstream complementary metal-oxidesemiconductor (CMOS) neurons that are indispensable for realizing of sophisticated circuits, which are considered unsuitable for large-scale network implementation due to their large area and low efficiency. [9,10] As a result, it is urgently expected to develop functional neuronal devices that enable the achievements of the basic functions of spiking neurons including the integration behavior and the firing action with simple circuits. Fortunately, substantial improvements have been made in developing artificial neurons by utilizing the emerging memristive devices with volatile TS characteristics, in which the switching low resistance state (LRS) can be recovered back to its initial high resistance state (HRS) in a very short period of time without applied external reset voltage.…”

Section: Introductionmentioning

confidence: 99%

“…[ 8 ] Nevertheless, many neural devices used for SNNs are the mainstream complementary metal‐oxide‐semiconductor (CMOS) neurons that are indispensable for realizing of sophisticated circuits, which are considered unsuitable for large‐scale network implementation due to their large area and low efficiency. [ 9,10 ] As a result, it is urgently expected to develop functional neuronal devices that enable the achievements of the basic functions of spiking neurons including the integration behavior and the firing action with simple circuits.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neurons Using Ag−In−Zn−S/Sericin Peptide‐Based Threshold Switching Memristors for Spiking Neural Networks

He,

Yan,

Zhang

et al. 2023

Adv Elect Materials

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Memristive devices with threshold switching characteristics can be effectively utilized to mimic biological neurons acting as one of the key building blocks for constructing advanced hardware neural networks. In this work, the emulation of leaky integrate‐and‐fire memristive neuron is realized in one single cell with Ag/Ag−In−Zn−S/silk sericin/W architecture without the need for additional auxiliary circuits. The studied devices demonstrate excellent electrical properties, such as stably repeatable threshold switching, concentratedly low threshold voltage (≈0.4 V), and relatively small device‐to‐device variation. In addition, multiple neural features, such as leaky integrate‐and‐fire neuron functionality and strength‐modulated spike frequency characteristic, have been successfully emulated owing to the forming‐free volatile threshold switching effect. The stable volatile threshold switching behaviors and regular firing event may be attributed to the controllable metallic Ag filamentary mechanism. Furthermore, a solid accuracy of 91.44% of the pattern recognition of Modified National Institute of Standards and Technology (MNIST) data is obtained via a trained spiking neural network (SNN) based on the leaky integrate‐and‐fire behavior of sericin‐based device. These achievements shed light on the fact that employing sericin biomaterials has great application potential in advanced neuromorphic computation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%