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250813s2025 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202509083
|2 doi
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|a pubmed25n1529.xml
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|a (DE-627)NLM391121685
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|a (NLM)40801165
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Wang, Zhiqing
|e verfasserin
|4 aut
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| 245 |
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|a Long-Range Order and Strong Quantum Coupling Enabled Stable Carrier Transport for Reliable Neuromorphic Computing
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|c 2025
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 13.08.2025
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a © 2025 Wiley‐VCH GmbH.
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|a Bio-inspired neuromorphic computing based on memristors holds significant potential for performing massively parallel computational tasks with high accuracy. However, its practical application is significantly limited by poor reliability, primarily due to instability in carrier transport. Here, long-range ordered quantum dot (QD) superlattices with strong quantum coupling is presented to enable carrier transport stability and improve device reliability. Leveraging a data-assisted QD synthesis optimization loop, Cu12Sb4S13 QDs are synthesized with precisely controlled growth kinetics, crystal orientation, and surface chemistry. These QDs self-assemble into long-range ordered superlattices on flexible substrates, achieving a 56% reduction in inter-dot spacing (to 0.92 nm), aligned lattice orientations, and a 4.4-fold increase in carrier mobility. This architecture enables strong quantum coupling, effectively overcoming the limitations imposed by localized quantum-confined states. As a result, the QD-based memristors exhibit remarkable reliability, with variations below 0.1% over 8.4 × 107 s of continuous operation and 106 rapid read cycles. They further demonstrate linear potentiation and depression characteristics (vp = 2.03 and vd = 2.33), a wide conductance range (Gmax/Gmin = 264), and high recognition accuracy (93.31%) as validated by chip-level convolutional neural network simulations. This work establishes a robust and flexible platform for memristor-based neuromorphic computing, offering a promising route to overcoming critical challenges in device reliability and computational performance
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|a Journal Article
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|a long‐range order
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|a neuromorphic computing
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|a quantum dot superlattices
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| 650 |
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|a robust reliability
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| 650 |
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4 |
|a strong quantum coupling
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| 700 |
1 |
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|a Shen, Jie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Keqiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Jing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Qiao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yin, Zhiwen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hu, Zhi-Yi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zeng, Jianrong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Pengchao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Wen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Jing
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g (2025) vom: 13. Aug., Seite e09083
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnas
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| 773 |
1 |
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|g year:2025
|g day:13
|g month:08
|g pages:e09083
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|u http://dx.doi.org/10.1002/adma.202509083
|3 Volltext
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|a GBV_ILN_350
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|a AR
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|j 2025
|b 13
|c 08
|h e09083
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