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• W4288096051 abstract "Abstract Artificial synapses and neurons are crucial milestones for neuromorphic computing hardware, and memristors with resistive and threshold switching characteristics are regarded as the most promising candidates for the construction of hardware neural networks. However, most of the memristors can only operate in one mode, that is, resistive switching or threshold switching, and distinct memristors are required to construct fully memristive neuromorphic computing hardware, making it more complex for the fabrication and integration of the hardware. Herein, we propose a flexible dual‐mode memristor array based on core–shell CsPbBr 3 @graphdiyne nanocrystals, which features a 100% transition yield, small cycle‐to‐cycle and device‐to‐device variability, excellent flexibility, and environmental stability. Based on this dual‐mode memristor, homo‐material‐based fully memristive neuromorphic computing hardware—a power‐free artificial nociceptive signal processing system and a spiking neural network—are constructed for the first time. Our dual‐mode memristors greatly simplify the fabrication and integration of fully memristive neuromorphic systems." @default.
• W4288096051 created "2022-07-28" @default.
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• W4288096051 date "2022-07-28" @default.
• W4288096051 modified "2023-10-14" @default.
• W4288096051 title "Optically modulated dual‐mode memristor arrays based on core‐shell CsPbBr ₃ @graphdiyne nanocrystals for fully memristive neuromorphic computing hardware" @default.
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• W4288096051 doi "https://doi.org/10.1002/smm2.1135" @default.
• W4288096051 hasPublicationYear "2022" @default.
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