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      A flexible ultrasensitive optoelectronic sensor array for neuromorphic vision systems

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          Abstract

          The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 10 7 A/W and a specific detectivity of 2 × 10 16 Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm 2.

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          Most cited references52

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Ultrasensitive photodetectors based on monolayer MoS2.

            Two-dimensional materials are an emerging class of new materials with a wide range of electrical properties and potential practical applications. Although graphene is the most well-studied two-dimensional material, single layers of other materials, such as insulating BN (ref. 2) and semiconducting MoS2 (refs 3, 4) or WSe2 (refs 5, 6), are gaining increasing attention as promising gate insulators and channel materials for field-effect transistors. Because monolayer MoS2 is a direct-bandgap semiconductor due to quantum-mechanical confinement, it could be suitable for applications in optoelectronic devices where the direct bandgap would allow a high absorption coefficient and efficient electron-hole pair generation under photoexcitation. Here, we demonstrate ultrasensitive monolayer MoS2 phototransistors with improved device mobility and ON current. Our devices show a maximum external photoresponsivity of 880 A W(-1) at a wavelength of 561 nm and a photoresponse in the 400-680 nm range. With recent developments in large-scale production techniques such as liquid-scale exfoliation and chemical vapour deposition-like growth, MoS2 shows important potential for applications in MoS2-based integrated optoelectronic circuits, light sensing, biomedical imaging, video recording and spectroscopy.
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              Quantum Dot Light-Emitting Diodes Based on Inorganic Perovskite Cesium Lead Halides (CsPbX3 ).

              Novel quantum-dot light-emitting diodes based on all-inorganic perovskite CsPbX3 (X = Cl, Br, I) nanocrystals are reported. The well-dispersed, single-crystal quantum dots (QDs) exhibit high quantum yields, and tunable light emission wavelength. The demonstration of these novel perovskite QDs opens a new avenue toward designing optoelectronic devices, such as displays, photodetectors, solar cells, and lasers.
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                Author and article information

                Contributors
                Journal
                Nature Communications
                Nat Commun
                Springer Science and Business Media LLC
                2041-1723
                December 2021
                March 19 2021
                December 2021
                : 12
                : 1
                Article
                10.1038/s41467-021-22047-w
                24236970-afb5-4894-bdab-e7d34593f199
                © 2021

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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