A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system

Rui, Yuan; Duan, Qingxi; Tiw, Pek Jun; Li, Ge; Xiao, Zhuojian; Jing, Zhaokun; Ke, Yang; Liu, Chang; Chen, Ge; Huang, Ru; Yang, Yuchao

doi:10.1038/s41467-022-31747-w

Cited by 132 publications

(109 citation statements)

References 67 publications

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“…Therefore, the statistical analysis of our MSC-VFET indicates a high SNR of 114.15 dB when the applied gate pulses are −60 V and the valid read-out I DS is set as 0.02 mA cm −2 , which is far beyond most reported experimental cases. [64,65]…”

Section: Resultsmentioning

confidence: 99%

Ultralow‐Power Vertical Transistors for Multilevel Decoding Modes

Zhang

Wang

et al. 2022

Advanced Materials

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addressing probabilistic, and unstructured problems, thus is anticipated to solve the von Neumann bottleneck and become the mainstay of contemporary computing systems. [1,2] Synapses serve a crucial function in signal transmission in the biological neural system. [3] They transmit signals through neurotransmitters in a manner similar to that of field-effect transistors, where source-drain current changing in response to changes in gate voltage. In addition, organic materials are solutionprocessable, [4][5][6][7] thus simulating the functions of biological synapses by organic field-effect transistors has received intensive attention. [8,9] Most recently, higher performance requirements for devices are put forward to support practical applications, including fast switching speed, low energy consumption, low operating voltage, and strong compatibility with flexible substrate. Traditional planar field effect transistors struggle to accomplish these functions. Conversely, vertical organic field-effect transistors (VOFETs) have a more stable sourcedrain current and a changeable channel length that depends Organic field-effect transistors with parallel transmission and learning functions are of interest in the development of brain-inspired neuromorphic computing. However, the poor performance and high power consumption are the two main issues limiting their practical applications. Herein, an ultralowpower vertical transistor is demonstrated based on transition-metal carbides/ nitrides (MXene) and organic single crystal. The transistor exhibits a high J ON of 16.6 mA cm −2 and a high J ON /J OFF ratio of 9.12 × 10 5 under an ultralow working voltage of −1 mV. Furthermore, it can successfully simulate the functions of biological synapse under electrical modulation along with consuming only 8.7 aJ of power per spike. It also permits multilevel information decoding modes with a significant gap between the readable time of professionals and nonprofessionals, producing a high signal-to-noise ratio up to 114.15 dB. This work encourages the use of vertical transistors and organic single crystal in decoding information and advances the development of low-power neuromorphic systems.

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Section: Resultsmentioning

confidence: 99%

Ultralow‐Power Vertical Transistors for Multilevel Decoding Modes

Zhang

Wang

et al. 2022

Advanced Materials

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“…Recently, emerging devices, such as memristors, have been used to emulate the functionalities of synapses and neurons. Yuan et al 91 reported a neuromorphic perception system that can monitor the curvatures of fingers by using the perception component VO 2 (Fig. 6b ).…”

Section: Applications Of Neuron Devicesmentioning

confidence: 99%

“… a Scheme of the biological perception system. b Response of artificial spiking curvature sensory neurons to the gesture ‘Two’ and corresponding statistics of spiking frequency 91 . Copyright 2022 Springer Nature.…”

Section: Applications Of Neuron Devicesmentioning

confidence: 99%

“…In biological perception systems, certain types of neurons and receptors, such as photoreceptors and mechanoreceptors, transform external environmental signals into electrical spikes (Fig. 6a) [85][86][87][88][89][90][91] . Artificial sensory neuron devices can mimic complicated sensing and processing functions in biological systems, which can convert external stimuli into electrical signals.…”

Section: Artificial Sensory Neuron Devicesmentioning

confidence: 99%

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Neuron devices: emerging prospects in neural interfaces and recognition

et al. 2022

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Neuron interface devices can be used to explore the relationships between neuron firing and synaptic transmission, as well as to diagnose and treat neurological disorders, such as epilepsy and Alzheimer’s disease. It is crucial to exploit neuron devices with high sensitivity, high biocompatibility, multifunctional integration and high-speed data processing. During the past decades, researchers have made significant progress in neural electrodes, artificial sensory neuron devices, and neuromorphic optic neuron devices. The main part of the review is divided into two sections, providing an overview of recently developed neuron interface devices for recording electrophysiological signals, as well as applications in neuromodulation, simulating the human sensory system, and achieving memory and recognition. We mainly discussed the development, characteristics, functional mechanisms, and applications of neuron devices and elucidated several key points for clinical translation. The present review highlights the advances in neuron devices on brain-computer interfaces and neuroscience research.

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“…For example, an optic-neural synaptic device based on an optical sensing transistor and a synaptic transistor connected in series was proposed, which is able to classify color-mixed patterns with a full-connected optic-neural network 20 . More recently, the artificial spiking receptors has aroused great interesting based on a consensus that information implied in rate is extremely energy-efficient and very robust to noise [23][24][25][26][27][28][29][30] . A spiking photoreceptor based on an optical sensor and oscillation neuron in series exhibited efficient edge image segmentation out of a complex background, representing a pioneer and feasible approach toward spiking cone photoreceptor 10 .…”

Section: Introductionmentioning

confidence: 99%

Vertical Integrated Spiking Cone Photoreceptor Arrays for Color Perception

Wan

Wang

Chen

et al. 2022

Preprint

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The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a vertical integrated spiking cone photoreceptor (VISCP) array with the structure of ITO/Ta2O5/Ag/IGZO/ITO, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. The VISCPs have an ultralow power consumption of ≤400 pW per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo three primary colors to form ‘colorful’ images for recognition tasks, and the device with the ability to discriminate mixed colors shows a better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.

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A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system

Cited by 132 publications

References 67 publications

Ultralow‐Power Vertical Transistors for Multilevel Decoding Modes

Ultralow‐Power Vertical Transistors for Multilevel Decoding Modes

Neuron devices: emerging prospects in neural interfaces and recognition

Vertical Integrated Spiking Cone Photoreceptor Arrays for Color Perception

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