Optically modulated dual‐mode memristor arrays based on core‐shell CsPbBr3@graphdiyne nanocrystals for fully memristive neuromorphic computing hardware
Fu‐Dong Wang,
Mei‐Xi Yu,
Xu‐Dong Chen,
Jiaqiang Li,
Zhi‐Cheng Zhang,
Yuan Li,
Guo‐Xin Zhang,
Ke Shi,
Lei Shi,
Min Zhang,
Tong‐Bu Lu,
Jin Zhang
Affiliations
Fu‐Dong Wang
MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering Tianjin University of Technology Tianjin China
Mei‐Xi Yu
MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering Tianjin University of Technology Tianjin China
Xu‐Dong Chen
MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering Tianjin University of Technology Tianjin China
Jiaqiang Li
Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering Peking University Beijing China
Zhi‐Cheng Zhang
The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics Nankai University Tianjin China
Yuan Li
MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering Tianjin University of Technology Tianjin China
Guo‐Xin Zhang
MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering Tianjin University of Technology Tianjin China
Ke Shi
MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering Tianjin University of Technology Tianjin China
Lei Shi
MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering Tianjin University of Technology Tianjin China
Min Zhang
MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering Tianjin University of Technology Tianjin China
Tong‐Bu Lu
MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Materials Science and Engineering Tianjin University of Technology Tianjin China
Jin Zhang
Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering Peking University Beijing China
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.
