Optically modulated dual‐mode memristor arrays based on core‐shell CsPbBr3@graphdiyne nanocrystals for fully memristive neuromorphic computing hardware. Issue 1 (28th July 2022) - Wang, Fu‐Dong; Yu, Mei‐Xi; Chen, Xu‐Dong; Li, Jiaqiang; Zhang, Zhi‐Cheng; Li, Yuan; Zhang, Guo‐Xin; Shi, Ke; Shi, Lei; Zhang, Min; Lu, Tong‐Bu; Zhang, Jin
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 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. Abstract : An optically modulated dual‐mode memristor array based on core–shell CsPbBr3 @graphdiyne nanocrystals is developed, which can emulate both the artificial synapses and neurons. Based on this dual‐mode memristor, homo‐material‐based fully memristive neuromorphic computing hardware including a power‐free artificial nociceptive signal processing system and a spiking neural network are constructed for the first time.