Electronic/ionic, plasmonic and photonic neuromorphic device concepts for artificial neural networks Emil List-Kratochvil Humboldt-Universität zu Berlin, Germany Artificial neural networks (ANN), inspired by biological nervous systems, enable signal processing beyond the capabilities of von Neumann computer architectures. Through dynamically adapting the connectivity (synaptic weights) in individual devices and by applying learning algorithms ANNs can offer in memory and tensor computing capabilities. Yet, to fully unleash the potential of hardware ANNs there is still a need for neuromorphic device concepts, which properly emulate all necessary synaptic functions adequately and allow for an easy integration into large scale hardware ANNs. In this contribution we will demonstrate novel electronic/ionic, plasmonic and photonic neuromorphic single device concepts and integration into photonic ANNs using hybrid material systems.
This work was supported by the Deutsche Forschungsgemeinschaft through the CRC 951.
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© The Author(s), 2023
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