Developing near-infrared quantum-dot light-emitting diodes to mimic synaptic plasticity

Zhao, Shuangyi; Wang, Yue; Huang, Wen; Jin, Hao; Huang, P. C.; Wang, Hu; Wang, Kun; Li, Dongsheng; Xu, Mingsheng; Yang, Deren; Pi, Xiaodong

doi:10.1007/s40843-019-9437-9

Cited by 34 publications

(29 citation statements)

References 74 publications

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“…Zhao et al [ 124 ] subsequently reported the use of NIR QD light‐emitting diodes (QLEDs) as optoelectronic synaptic devices (Figure 8d) given the advantage of the low loss of NIR light in several technologically important materials. These NIR QLEDs had the multilayered structure of Ag/ZnO/Si NCs/PFN/P3HT /PEDOT:PSS/ITO/glass with the EL peak at the wavelength of ≈850 nm.…”

Section: Categories Of Optoelectronic Synaptic Devicesmentioning

confidence: 99%

Optoelectronic Synaptic Devices for Neuromorphic Computing

Wang

Yin

Huang

et al. 2020

Advanced Intelligent Systems

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195

196

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Neuromorphic computing can potentially solve the von Neumann bottleneck of current mainstream computing because it excels at self‐adaptive learning and highly parallel computing and consumes much less energy. Synaptic devices that mimic biological synapses are critical building blocks for neuromorphic computing. Inspired by recent progress in optogenetics and visual sensing, light has been increasingly incorporated into synaptic devices. This paves the way to optoelectronic synaptic devices with a series of advantages such as wide bandwidth, negligible resistance–capacitance (RC) delay and power loss, and global regulation of multiple synaptic devices. Herein, the basic functionalities of synaptic devices are introduced. All kinds of optoelectronic synaptic devices are then discussed by categorizing them into optically stimulated synaptic devices, optically assisted synaptic devices, and synaptic devices with optical output. Existing practical scenarios for the application of optoelectronic synaptic devices are also presented. Finally, perspectives on the development of optoelectronic synaptic devices in the future are outlined.

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Section: Categories Of Optoelectronic Synaptic Devicesmentioning

confidence: 99%

Optoelectronic Synaptic Devices for Neuromorphic Computing

Wang

Yin

Huang

et al. 2020

Advanced Intelligent Systems

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195

196

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“…It has been envisioned that high-performance neuromorphic computing (i.e., brain-like computing) will seriously demand optoelectronically integrated artificial neural networks. − For the sake of the large-scale deployment of high-performance neuromorphic computing in the future, it would be advantageous to build optoelectronically integrated artificial neural networks by using advanced silicon (Si) technologies such as Si photonics . This calls for the development of optoelectronic synaptic devices based on Si materials. − …”

mentioning

confidence: 99%

Optically Stimulated Synaptic Devices Based on the Hybrid Structure of Silicon Nanomembrane and Perovskite

et al. 2020

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Optoelectronic synaptic devices have been attracting increasing attention due to their critical role in the development of neuromorphic computing based on optoelectronic integration. Here we start with silicon nanomembrane (Si NM) to fabricate optoelectronic synaptic devices. Organolead halide perovskite (MAPbI 3 ) is exploited to form a hybrid structure with Si NM. We demonstrate that synaptic transistors based on the hybrid structure are very sensitive to optical stimulation with low energy consumption. Synaptic functionalities such as excitatory post-synaptic current (EPSC), paired-pulse facilitation, and transition from short-term memory to long-term memory (LTM) are all successfully mimicked by using these optically stimulated synaptic transistors. The backgate-enabled tunability of the EPSC of these devices further leads to the LTM-based mimicking of visual learning and memory processes under different mood states. This work contributes to the development of Si-based optoelectronic synaptic devices for neuromorphic computing.

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“…In 2019, Pi et al [48] developed an NIR QD light-emitting diodes based on P3HT and poly[9,9-bis(3′-(N,Ndimethylamino)propyl)-2,7-flourene]-alt-2,7-(9,9-dioctylfluorene)] (PFN). The maximum UV-vis absorption peak of P3HT is at around 450nm [49] and the bandgap of P3HT utilized in this work is 2.0 eV.…”

Section: Mixed Composition Of Organic Semiconductors and Perovskite Quantum Dotsmentioning

confidence: 99%

Organic Field Effect Transistor‐Based Photonic Synapses: Materials, Devices, and Applications

Zhang

Jin

et al. 2021

Adv Funct Materials

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Photonic artificial synapses-based neuromorphic computing systems have been regarded as promising candidates for replacing von Neumann-based computing systems due to the high bandwidth, ultrafast signal transmission, low energy consumption, and wireless communication. Although significant progress has been made in developing varied device structures for synaptic emulation, organic field-effect transistors (OFETs) hold the compelling advantages of facile preparation, liable integration, and versatile structures. As a powerful and effective platform for photonic synapses, OFETs can fulfill not only the simulation of simple synaptic functions, but also complex photoelectric dual modulation and simulation of the visual system. Herein, an overview of OFET-based photonic synapses, including functional materials, device configurations, and innovative applications is provided. Meanwhile, rules for selecting materials, mechanism of photoelectric conversion, and fabrication techniques of devices are also highlighted. Finally, challenges and opportunities are all discussed, providing solid guidance for multilevel memory, multi-functional tandem artificial neural system, and artificial intelligence.

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Developing near-infrared quantum-dot light-emitting diodes to mimic synaptic plasticity

Cited by 34 publications

References 74 publications

Optoelectronic Synaptic Devices for Neuromorphic Computing

Optoelectronic Synaptic Devices for Neuromorphic Computing

Optically Stimulated Synaptic Devices Based on the Hybrid Structure of Silicon Nanomembrane and Perovskite

Organic Field Effect Transistor‐Based Photonic Synapses: Materials, Devices, and Applications

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