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Journal Article | FZJ-2021-00670 |
;
2020
Nature Publishing Group
London [u.a.]
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Please use a persistent id in citations: http://hdl.handle.net/2128/26963 doi:10.1038/s41565-020-0702-9
Abstract: Nanoionic memrisitve devices are one of the most promising building blocks for next generation hardware architectures for cognitive type data processing. These highly scalable, low power, fast operating units offer a broad spectrum of functionalities at various operation conditions. This makes them ideal for direct applications such as sensors, selectors, short and long-term memories and more complex systems such as internet of things (IoT) and artificial intelligence (AI). Significant progress has been achieved implementing memristive devices in circuits for neuromorphic computing, demonstrating capabilities of pattern classifcation1, signal/image processing2, context-dependent network formation3, recognition of spatiotemporal patterns4 and so on. However, despite this highly encouraging progress the full potential of the memristive technologies is yet to be reached. The main advantage of the nanoionic memrisitve devices compared to classical semiconductor technologies is their operation principle, relying on redox reactions and transport of ions/atoms instead of electrons. This fact has been mostly underestimated during the race for fast integration and product developments, and fewer efforts have been dedicated to material design through a thorough understanding of the underlying physical processes.
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