A flexible ultrasensitive optoelectronic sensor array for neuromorphic vision systems

Zhu, Qian‐Bing; Li, Bo; Yang, Dandan; Sun, Deshun; Feng, Shun; Chen, Maolin; Sun, Yun; Tian, Yanan; Su, Xin; Wang, Xiaomu; Qiu, Song; Li, Qingwen; Li, Xiaoming; Zeng, Haibo; Cheng, Hui‐Ming; Sun, Dongming

doi:10.1038/s41467-021-22047-w

Cited by 279 publications

(137 citation statements)

References 52 publications

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“…To explore the application prospects of perovskite in large-scale integrated photoelectric bionic devices, Sun et al. proposed a 32 × 32 flexible optoelectronic transistor array based on perovskite quantum dots and carbon nanotubes ( Figures 8 E and 8F), which combines the functions of the photodetector and synaptic electronic with high responsivity and synaptic plasticity regulation ability ( Zhu et al., 2021 ). As shown in Figure 8 G, both the number and intensity of light pulses can gradually increase the conductivity of the array, which exhibits the human brain-like reinforcement learning function.…”

Section: Advanced Machine Vision System With Retinomorphic Optoelectronic Devicesmentioning

confidence: 99%

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Retinomorphic optoelectronic devices for intelligent machine vision

2022

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Summary Biological visual system can efficiently handle optical information within the retina and visual cortex of the brain, which suggests an alternative approach for the upgrading of the current low-intelligence, large energy consumption, and complex circuitry of the artificial vision system for high-performance edge computing applications. In recent years, retinomorphic machine vision based on the integration of optoelectronic image sensors and processors has been regarded as a promising candidate to improve this phenomenon. This novel intelligent machine vision technology can perform information preprocessing near or even within the sensor in the front end, thereby reducing the transmission of redundant raw data and improving the efficiency of the back-end processor for high-level computing tasks. In this contribution, we try to present a comprehensive review on the recent progress achieved in this emergent field.

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Section: Advanced Machine Vision System With Retinomorphic Optoelectronic Devicesmentioning

confidence: 99%

“… (G) Measured training weight results of a number 8 pattern along with increasing pulse number and light intensity. Reproduced with permission ( Zhu et al., 2021 ). Copyright 2021, Springer Nature.…”

Section: Advanced Machine Vision System With Retinomorphic Optoelectronic Devicesmentioning

confidence: 99%

Retinomorphic optoelectronic devices for intelligent machine vision

2022

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“…Similarities between biological structures and artificial neural-like networks have inspired the development of neuron-based mechanical sensing and optical sensing [9][10][11]. The olfactory neurons that exist in the olfactory bulb of higher animals play a key role in extracting olfactory signals through gas sensing [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Artificial Neuron-Like Gas Sensor Constructed from CuS Quantum Dots/Bi2S3 Nanosheets

et al. 2021

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Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring. Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites, leading to a very low gas adsorption ability. Moreover, the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior. In this work, a gas sensing structure model based on CuS quantum dots/Bi2S3 nanosheets (CuS QDs/Bi2S3 NSs) inspired by artificial neuron network is constructed. Simulation analysis by density functional calculation revealed that CuS QDs and Bi2S3 NSs can be used as the main adsorption sites and charge transport pathways, respectively. Thus, the high-sensitivity sensing of NO2 can be realized by designing the artificial neuron-like sensor. The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable, which can enhance the NO2 sensitivity due to the rich sensitive sites and quantum size effect. The Bi2S3 NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission. The neuron-like sensor that simulates biological smell shows a significantly enhanced response value (3.4), excellent responsiveness (18 s) and recovery rate (338 s), low theoretical detection limit of 78 ppb, and excellent selectivity for NO2. Furthermore, the developed wearable device can also realize the visual detection of NO2 through real-time signal changes.

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“…So far, a variety of neuromorphic visual systems have been developed for implementing typical image processing functionalities including the contrast enhancement, noise suppression, adaptive imaging, recognition, and auto-encoding [5][6][7][8][9][10][11][12][13][14][15] . Among these systems, reconfigurable photosensor network (PS-NET) with an in-sensor computing architecture (Fig.…”

Section: Introductionmentioning

confidence: 99%

Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision

Cui¹,

Fan²,

Li³

et al. 2021

Preprint

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Nowadays the development of machine vision is oriented toward real-time applications such as autonomous driving. This demands a hardware solution with low latency, high energy efficiency, and good reliability. Here, we demonstrate a robust and self-powered in-sensor computing paradigm with a ferroelectric photosensor network (FE-PS-NET). The FE-PS-NET, constituted by ferroelectric photosensors (FE-PSs) with tunable photoresponsivities, is capable of simultaneously capturing and processing images. In each FE-PS, self-powered photovoltaic responses, modulated by remanent polarization of an epitaxial ferroelectric Pb(Zr0.2Ti0.8)O3 layer, show not only multiple nonvolatile levels but also a sign reversibility, enabling the representation of a signed weight in a single device and hence reducing the hardware overhead for network construction. With multiple FE-PSs wired together, the FE-PS-NET acts on its own as an artificial neural network. It is demonstrated that an in situ multiply-accumulate operation between an input image and a stored photoresponsivity matrix is available in our FE-PS-NET hardware. The FE-PS-NET hardware is faultlessly competent for real-time image processing functionalities, including binary pattern classification with an accuracy of 100% and edge detection with an F-Measure of 95.2%. This study highlights the great potential of ferroelectric photovoltaics as the hardware basis of real-time machine vision.

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A flexible ultrasensitive optoelectronic sensor array for neuromorphic vision systems

Cited by 279 publications

References 52 publications

Retinomorphic optoelectronic devices for intelligent machine vision

Retinomorphic optoelectronic devices for intelligent machine vision

A Novel Artificial Neuron-Like Gas Sensor Constructed from CuS Quantum Dots/Bi2S3 Nanosheets

Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision

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