Wireless Multiferroic Memristor with Coupled Giant Impedance and Artificial Synapse Application

Abstract Internet of things (IoT) becomes part of everyday life across the globe, whose nodes are able to sense, store, and transmit information wirelessly. However, the IoT nodes based on von Neumann architectures realize the memory, computing and communication functions with physical separated devices, which result in severe power consumption and computation latency. In this study, a wireless multiferroic memristor consisting of Metglas/Pb(Zr 0.3 Ti 0.7 )O 3 ‐1 mol% Mn/Metglas laminate is proposed, which integrates the storage, processing, and wireless communication of information in a single device at the first time. First, a power efficient impedance modulation mechanism is explored for the multiferroic memristor, which couples electric field modulated giant magnetoimpedance (GMI) of magnetostrictive Metglas with varied impedance of ferroelectric Pb(Zr 0.3 Ti 0.7 )O 3 ‐1 mol% Mn due to the gradual polarization switching. Meanwhile a pair of multiferroic memristors are used to transmit and receive stored information wirelessly, which facilitate the application of memristor in the IoT. Furthermore, the experimental study demonstrates that the memristor can mimic the synaptic plasticity, such as long‐term potentiation, depression, and spiking‐timing‐dependent plasticity, and it also reveals the capability of pattern learning with a memristor network. This work paves a way toward the IoT nodes integrating both brain inspired computing and wireless communication functions.

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