Electrochemical ohmic memristors for continual learning
Nature Communications, vol. 16, pp. 2348
Abstract
Developing versatile and reliable memristive devices is crucial for advancing future memory and computing architectures. The years of intensive research have still not reached and demonstrated their full horizon of capabilities, and new concepts are essential for successfully using the complete spectra of memristive functionalities for industrial applications. Here, we introduce two-terminal ohmic memristor, characterized by a different type of switching defined as filament conductivity change mechanism (FCM). The operation is based entirely on localized electrochemical redox reactions, resulting in essential advantages such as ultra-stable binary and analog switching, broad voltage stability window, high temperature stability, high switching ratio and good endurance. The multifunctional properties enabled by the FCM can be effectively used to overcome the catastrophic forgetting problem in conventional deep neural networks. Our findings represent an important milestone in resistive switching fundamentals and provide an effective approach for designing memristive system, expanding the horizon of functionalities and neuroscience applications.
Authors 6
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RWTH Aachen University · National Institute for Materials Science
Affiliation as printed
Institute of Materials in Electrical Engineering 2 (IWE2), RWTH Aachen University, Aachen, Germany. CHEN.Shaochuan@nims.go.jp
International Center for Young Scientists (ICYS), National Institute for Materials Science (NIMS), Tsukuba, Japan. CHEN.Shaochuan@nims.go.jp
Institute of Materials in Electrical Engineering 2 (IWE2), RWTH Aachen University, Aachen, Germany
International Center for Young Scientists (ICYS), National Institute for Materials Science (NIMS), Tsukuba, Japan
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Affiliation as printed
Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China
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Affiliation as printed
Central Institute for Engineering, Electronics and Analytics (ZEA-3), Forschungszentrum Jülich, Jülich, Germany
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Affiliation as printed
Central Institute for Engineering, Electronics and Analytics (ZEA-3), Forschungszentrum Jülich, Jülich, Germany
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Yuchao Yang corresponding
Peking University · Chinese Institute for Brain Research
Affiliation as printed
Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China. yuchaoyang@pku.edu.cn
Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, Beijing, China. yuchaoyang@pku.edu.cn
Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, China. yuchaoyang@pku.edu.cn
Guangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China. yuchaoyang@pku.edu.cn
Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, Beijing, China
Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, China
Guangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Peking University, Shenzhen, China
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Ilia Valov corresponding
Forschungszentrum Jülich · Bulgarian Academy of Sciences · Jülich Aachen Research Alliance · Institute of Electrochemistry and Energy Systems
Affiliation as printed
Institute of Electrochemistry and Energy Systems, Bulgarian Academy of Sciences, Sofia, Bulgaria. i.valov@fz-juelich.de
Peter Grünberg Institute 7 and JARA-FIT, Forschungszentrum Jülich, Jülich, Germany. i.valov@fz-juelich.de
Institute of Electrochemistry and Energy Systems, Bulgarian Academy of Sciences, Sofia, Bulgaria
Peter Grünberg Institute 7 and JARA-FIT, Forschungszentrum Jülich, Jülich, Germany
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