Ruibo Ai, Wang Luo, Xiaojun Liu, Tao Zhang, Jiqun Sang, Yaolin Zhang
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引用次数: 0
Abstract
In the era of artificial intelligence, there has been a rise in novel computing methods due to the increased demand for rapid and effective data processing. It is of great significance to develop memristor devices capable of emulating the computational neural network of the brain, especially in the realm of artificial intelligence applications. In this work, a memristor based on NiAl-layered double hydroxides is presented with excellent electrical performance, including analog resistive conversion characteristics and the effect of multi-level conductivity modulation. In addition, the device's conductance can be continuously adjusted by varying pulse width, interval, and amplitude. The successful replication of synaptic features has been achieved. In order to implement the functions of "NOT," "AND," and "OR," a logic gate is constructed using two synaptic devices. The confirmation of the potential use of synaptic devices in brain-like computing was demonstrated. In addition, it demonstrates the potential of these devices in supporting computing models beyond von Neumann architecture.
期刊介绍:
The Journal of Chemical Physics publishes quantitative and rigorous science of long-lasting value in methods and applications of chemical physics. The Journal also publishes brief Communications of significant new findings, Perspectives on the latest advances in the field, and Special Topic issues. The Journal focuses on innovative research in experimental and theoretical areas of chemical physics, including spectroscopy, dynamics, kinetics, statistical mechanics, and quantum mechanics. In addition, topical areas such as polymers, soft matter, materials, surfaces/interfaces, and systems of biological relevance are of increasing importance.
Topical coverage includes:
Theoretical Methods and Algorithms
Advanced Experimental Techniques
Atoms, Molecules, and Clusters
Liquids, Glasses, and Crystals
Surfaces, Interfaces, and Materials
Polymers and Soft Matter
Biological Molecules and Networks.