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 CsPbBr3@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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.