Organic iontronic memristors for artificial synapses and bionic neuromorphic computing

To tackle the current crisis of Moore's law, a sophisticated strategy entails the development of multistable memristors, bionic artificial synapses, logic circuits and brain-inspired neuromorphic computing. In comparison with conventional electronic systems, iontronic memristors offer greater p...

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Bibliographic Details
Published inNanoscale Vol. 16; no. 4; pp. 1471 - 1489
Main Authors Xia, Yang, Zhang, Cheng, Xu, Zheng, Lu, Shuanglong, Cheng, Xinli, Wei, Shice, Yuan, Junwei, Sun, Yanqiu, Li, Yang
Format Journal Article
LanguageEnglish
Published England Royal Society of Chemistry 25.01.2024
Subjects
Artificial intelligence
Bionics
Brain
Electronic systems
Logic circuits
Memory devices
Memristors
Moore's law
Neuromorphic computing
Synapses
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Summary:To tackle the current crisis of Moore's law, a sophisticated strategy entails the development of multistable memristors, bionic artificial synapses, logic circuits and brain-inspired neuromorphic computing. In comparison with conventional electronic systems, iontronic memristors offer greater potential for the manifestation of artificial intelligence and brain-machine interaction. Organic iontronic memristive materials (OIMs), which possess an organic backbone and exhibit stoichiometric ionic states, have emerged as pivotal contenders for the realization of high-performance bionic iontronic memristors. In this review, a comprehensive analysis of the progress and prospects of OIMs is presented, encompassing their inherent advantages, diverse types, synthesis methodologies, and wide-ranging applications in memristive devices. Predictably, the field of OIMs, as a rapidly developing research subject, presents an exciting opportunity for the development of highly efficient neuro-iontronic systems in areas such as in-sensor computing devices, artificial synapses, and human perception. Organic iontronic memristors are promising for high-density data storage, artificial synapses, and neuromorphic computing. This review provides a comprehensive summary of their concept, classification, preparation, mechanism, and application.
Bibliography:Yang Li is an associate professor at the School of Physical Science and Technology, Suzhou University of Science and Technology. He received his BS and PhD degrees from Soochow University, under the supervision of Prof. Jianmei Lu. From 2016 to 2017, he got financial support from the Chinese government to complete his joint PhD research at Nanyang Technological University, in Prof. Qichun Zhang's group. He is currently a Vebleo Fellow. His research interests focus on the preparation of novel organic/inorganic memristive materials and devices, and their applications for data storage and neuromorphic computing.
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ISSN:2040-3364
2040-3372
2040-3372
DOI:10.1039/d3nr06057h