A Configurable Artificial Neuron Based on a Threshold-Tunable TiN/NbOₓ/Pt Memristor

Wang, Yongzhou; Xu, Hui; Wang, Wei; Zhang, Xumeng; Wu, Zuheng; Gu, Ran; Li, Qingjiang; Liu, Qi

doi:10.1109/led.2022.3150034

Cited by 29 publications

(14 citation statements)

References 34 publications

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“…The DC I-V characteristics of the electroformed device with a compliance current of 2 mA are shown in figure 3(a). When the voltage exceeds the threshold voltage (V th ) of 4.6 V, the NbO 2 in the threshold region undergoes IMT at a high temperature due to Joule heating, which leads to a rapid variation in the conductivity, while it changes back when the voltage is below the hold voltage (V hold ) of 3.3 V [31,32]. To explore the conduction mechanism of the device, the sub-threshold I-V characteristic was fitted using the Poole-Frankel model, and the fitting results indicate that the conduction mechanism of the HRS is mainly dominated by Poole-Frankel conduction, which is consistent with previously reported NbO x [33,34].…”

Section: Resultsmentioning

confidence: 99%

Artificial visual neuron based on threshold switching memristors

Wen

Zhu²,

Guo³

2023

Neuromorph. Comput. Eng.

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The human visual system encodes optical information perceived by photoreceptors in the retina into neural spikes and then processes them by the visual cortex, with high efficiency and low energy consumption. Inspired by this information processing mode, an universal artificial neuron constructed with a resistor (Rs) and a threshold switching memristor can realize rate coding by modulating pulse parameters and the resistance of Rs. Owing to the absence of an external parallel capacitor, the artificial neuron has minimized chip area. In addition, an artificial visual neuron is proposed by replacing Rs in the artificial neuron with a photo-resistor. The oscillation frequency of the artificial visual neuron depends on the distance between the photo-resistor and light, which is fundamental to acquiring depth perception for precise recognition and learning. A visual perception system with the artificial visual neuron can accurately and conceptually emulate the self-regulation process of the speed control system in driverless automobiles. Therefore, the artificial visual neuron can process efficiently sensory data, reduce or eliminate data transfer and conversion at sensor/processor interfaces, and expand its application in the field of artificial intelligence.

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Section: Resultsmentioning

confidence: 99%

Artificial visual neuron based on threshold switching memristors

Wen

Zhu²,

Guo³

2023

Neuromorph. Comput. Eng.

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“…Most of the above optimization efforts rely on external components, which is clearly detrimental to the circuit area. Wu et al [131] proposed a combined device, as shown in Fig. 8(h).…”

Section: Artificial Neuronmentioning

confidence: 99%

Volatile threshold switching memristor: An emerging enabler in the AIoT era

Zuo¹,

Fu²,

Zhang³

et al. 2023

J. Semicond.

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With rapid advancement and deep integration of artificial intelligence and the internet-of-things, artificial intelligence of things has emerged as a promising technology changing people’s daily life. Massive growth of data generated from the devices challenges the AIoT systems from information collection, storage, processing and communication. In the review, we introduce volatile threshold switching memristors, which can be roughly classified into three types: metallic conductive filament-based TS devices, amorphous chalcogenide-based ovonic threshold switching devices, and metal-insulator transition based TS devices. They play important roles in high-density storage, energy efficient computing and hardware security for AIoT systems. Firstly, a brief introduction is exhibited to describe the categories (materials and characteristics) of volatile TS devices. And then, switching mechanisms of the three types of TS devices are discussed and systematically summarized. After that, attention is focused on the applications in 3D cross-point memory technology with high storage-density, efficient neuromorphic computing, hardware security (true random number generators and physical unclonable functions), and others (steep subthreshold slope transistor, logic devices, etc.). Finally, the major challenges and future outlook of volatile threshold switching memristors are presented.

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“…15 Some works have built artificial neurons based on volatile threshold memristors, realizing basic neuron functions, such as threshold-driven firing, all-or-nothing spikes, refractory periods, and intensity-modulated frequency response. 16–19 But all these are only in a positive direction 16–18 or negative direction. 19 Consequently, it is urgent to construct an artificial neuron that can realize neuron functions in both directions.…”

Section: Introductionmentioning

confidence: 99%

“…16–19 But all these are only in a positive direction 16–18 or negative direction. 19 Consequently, it is urgent to construct an artificial neuron that can realize neuron functions in both directions.…”

Section: Introductionmentioning

confidence: 99%

A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbO_x volatile threshold devices with ultra-low operating current

Zhao,

Tong,

Niu

et al. 2023

Nanoscale

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A Configurable Artificial Neuron Based on a Threshold-Tunable TiN/NbOₓ/Pt Memristor

Cited by 29 publications

References 34 publications

Artificial visual neuron based on threshold switching memristors

Artificial visual neuron based on threshold switching memristors

Volatile threshold switching memristor: An emerging enabler in the AIoT era

A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbO_x volatile threshold devices with ultra-low operating current

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A Configurable Artificial Neuron Based on a Threshold-Tunable TiN/NbOₓ/Pt Memristor

Cited by 29 publications

References 34 publications

Artificial visual neuron based on threshold switching memristors

Artificial visual neuron based on threshold switching memristors

Volatile threshold switching memristor: An emerging enabler in the AIoT era

A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbOx volatile threshold devices with ultra-low operating current

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A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbO_x volatile threshold devices with ultra-low operating current