Matter
·
2024
·
IF 17.3
Cong Chen,Zhenjia Chen,Di Liu,Xianghong Zhang,Changsong Gao,Liuting Shan,Lujian Liu,Tianjian Chen,Tailiang Guo,Huipeng Chen
Machine vision enables machines to extract rich information from image or video data and make intelligent decisions. However, approaches using artificial synapse hardware systems significantly limit the real-time and accuracy in machine vision segmentation amid complex environments. Addressing this, we propose a novel three-terminal adaptive artificial-light-emitting synapse (AALS) capable of photoelectric double output along with adaptive behavior. The device uses silver nanowires (AgNWs) as polar conductive bridges to reduce reliance on transparent electrodes, while polyvinyl alcohol (PVA) dielectric layers adaptively modulate charge carrier concentrations in conductive channels. Additionally, we have designed an adaptive parallel neural network (APNN) and applied it to autonomous driving image processing. This innovation significantly reduces adaptation time and notably enhances mean pixel accuracy (MPA) for semantic segmentation under overexposure and low-light conditions by 142.2% and 304.4%, respectively. Therefore, this work introduces new strategies for advanced adaptive vision, promising significant potential in intelligent driving and neuromorphic computing.
研究领域:
材料科学
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神经形态计算
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计算机视觉
✓ 已验证
产品关联
DOI
高影响因子
Energy & Environmental Science
·
2022
·Vol. 15
·期 10
·4247-4258
·
IF 39.714
Zeyu Song,Jihuai Wu,Yongguang Tu,Liuxue Sun,Tingting Zhu,Guodong Li,Xiaobing Wang,Yitian Du,Chunyan Deng,Qi Chen,Weihai Sun,Miaoliang Huang,Leqing Fan,Yunfang Huang,Yuelin Wei,Yiming Xie,Yu Lin,Hongwei Chen,Jianming Lin,Lan Zhan,Peng Gao,Mohammad Khaja Nazeeruddin,Wei Huang
Photocapacitor integrating both energy harvest and storage functions into a single device is a frontier research orientation, which facilitates the efficient and sustainable utilization of green energy. However, the multi-functions in one device and structural complexity of the integrated device, particularly the mismatch between energy harvest and storage units, lead to a relatively large energy loss in the energy storage and output processes. Here, we design a voltage adjustable hybrid supercapacitor (VAHSC) as an energy storage unit of a three-terminal photocapacitor. The VAHSC effectively harmonizes the energy harvest and storage units, resulting in the current, voltage, power, and energy match between both units. The optimal photocapacitor achieves a storage efficiency as high as 98.28% and Joule efficiency of 86.01%, along with excellent charge/discharge cycle stability. The great leap in this efficiency marks a substantial step towards the practical application of solar-charging energy storage integrated devices.
研究领域:
能量存储与转换
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太阳能
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材料科学
·
电化学
✓ 已验证
产品关联
DOI
高影响因子
ADVANCED FUNCTIONAL MATERIALS
·
2022
·Vol. 32
·期 21
·2107314
·
IF 19.924
Zhichao Xie,Chenyu Zhuge,Yanfei Zhao,Wei Xiao,Yujun Fu,Dongliang Yang,Shunpeng Zhang,Yingtao Li,Qi Wang,Yazhou Wang,Wan Yue,Iain McCulloch,Deyan He
Artificial synaptic devices are the basic composition units for neuromorphic computing processors that realize massive parallel computing. However, the n-type organic transistors have failed to achieve good performance as an artificial synaptic device for neuromorphic computing until now. Here, a vertical three-terminal n-type organic artificial synapse (TNOAS) using a lithium ion-based organic dielectric and the n-type donor–acceptor (D–A) conjugated polymer-naphthalene-1,4,5,8-tetracarboxylic-diimide-thiophene-vinyl-thiophene (NDI-gTVT) as the channel is proposed. The TNOAS achieves nonvolatile conductance modulation with high current density operation (≈10 KA cm −2 ) at low voltage and mimics the basic functions of biological synapses, such as long-term synaptic plasticity and paired-pulse facilitation. The minimum energy consumption of a response event triggered by a single action potential is 6.16 pJ, which can be comparable with p-type counterparts. Moreover, simulation using handwritten digital datasets exhibit a high recognition accuracy of 94%.
研究领域:
神经形态计算
·
有机电子学
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材料科学
·
电子器件
·
人工智能硬件
✓ 已验证
DOI
高影响因子
危险品化学品经营许可证（带存储）