2023
DOI: 10.1002/adma.202212118
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Porous Metal–Organic Framework/ReS2 Heterojunction Phototransistor for Polarization‐Sensitive Visual Adaptation Emulation

Abstract: Visual adaptation allows organisms to accurately perceive the external world even in dramatically changing environments, from dim starlight to bright sunlight. In particular, polarization-sensitive visual adaptation can effectively process the polarized visual information that is ubiquitous in nature. However, such an intriguing characteristic still remains a great challenge in semiconductor devices. Herein, a novel porous metal-organic-framework phototransistor with anisotropic-ReS 2 -based heterojunction is … Show more

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Cited by 41 publications
(23 citation statements)
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“…The energy consumption of the proposed synaptic device is analyzed, as it is an important metric for the NVS to perform neuromorphic computing. In general, it can be calculated as the following equation E = I normalp normale normala normalk × t × V normald normals where I peak is the peak current of EPSC under optical stimulation, t is the duration of a laser pulse, and V ds is the operating voltage applied to the drain source. Previous results have demonstrated that the EPSC is related to the illumination wavelength, duration, and power.…”
Section: Resultsmentioning
confidence: 99%
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“…The energy consumption of the proposed synaptic device is analyzed, as it is an important metric for the NVS to perform neuromorphic computing. In general, it can be calculated as the following equation E = I normalp normale normala normalk × t × V normald normals where I peak is the peak current of EPSC under optical stimulation, t is the duration of a laser pulse, and V ds is the operating voltage applied to the drain source. Previous results have demonstrated that the EPSC is related to the illumination wavelength, duration, and power.…”
Section: Resultsmentioning
confidence: 99%
“…Inspired by the human visual system (HVS) with low redundancy, low latency, high dynamics, and adaptation, a neuromorphic vision system (NVS) based on optoelectronic synapses is being considered to provide an effective path to break through the bottleneck of the conventional artificial vision system (AVS). Currently, NVSs exhibit powerful visual information preprocessing capabilities, such as image contrast enhancement, , image denoising, , feature extraction, , pattern recognition, and motion detection, thereby addressing the main problem of the traditional AVS, which demands vast computing resources to process gigantic redundant data . In situ storage has also been successfully achieved at the sensor end owing to optoelectronic synapses that mimic visual learning and memory, thereby considerably reducing the dependence on system memory. Moreover, optoelectronic synapses with visual adaptation can actively adjust NVSs to match new visual tasks in varying environments and successfully adapt to bright/dark conditions and various visual angles. To achieve ultrafast vision, based on the nonlinear photoresponse and positive/negative photoconductive behaviors of optoelectronic synapses, NVSs can handle inputting visual information at the sensors end in real time via reservoir computing and convolution operation, which can address high transmission delay limitation of the AVS with separated architectures. …”
Section: Introductionmentioning
confidence: 99%
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“…[ 29–31 ] Up to now, MOFs have been believed to a cutting‐edge porous material after zeolite, carbon nanotubes, and have been extensively employed in catalyst, energy storage, and adsorption, and so forth. [ 32–34 ] Due to the unique properties of MOFs, different MOFs have been employed to modify fibers for advanced tribological performances. [ 35,36 ] Shan et al [ 37 ] used an in situ growth strategy to grow ZIF‐8 nanocrystals at CF to enhance interfacial binding between CF and the polyhexahydrotriazine (PHT) matrix.…”
Section: Introductionmentioning
confidence: 99%
“…), and 2D vdWMs ( e.g. , black phosphorus, 13 MoS 2 , 14,15 WSe 2 , 15 Bi 2 O 2 Se, 16 ReSe 2 , 17 CrTe 2 , 18 Ge 4 Se 9 , 19 ZnIn 2 S 4 , 20 AgInP 2 S 6 , 21 etc. ).…”
Section: Introductionmentioning
confidence: 99%