DOI: 10.1002/adfm.202305869 ISSN: 1616-301X

Unidirectional Neuromorphic Resistive Memory Integrated with Piezoelectric Nanogenerator for Self‐Power Electronics

Muhammad Umair Khan, Yawar Abbas, Moh'd Rezeq, Anas Alazzam, Baker Mohammad
  • Electrochemistry
  • Condensed Matter Physics
  • Biomaterials
  • Electronic, Optical and Magnetic Materials


This study presents a method to enhance data processing by integrating a unidirectional analogue artificial neuromorphic memristor device with a piezoelectric nanogenerator, taking inspiration from biological information processing. A self‐powered unidirectional neuromorphic resistive memory device is proposed, comprising an ITO/ZnO/Yb2O3/Au structure combined with a high‐sensitivity piezoelectric nanogenerator (PENG) ITO/ZnO/Al. The memristor device is operated at a voltage sweep of ±4 V with a low operating current in a range of 1.4 µA. The filament formation is studied using a conductive mode atomic force microscope. The integration enables the creation of a self‐powered artificial sensing system that converts mechanical stimuli from the PENG into electrical signals, which are subsequently processed by analogue unidirectional neuromorphic device to mimic the functionality of a neuron without requiring additional circuitry. This emulation encompasses crucial functions such as potentiation, depression, and synaptic plasticity. Furthermore, this study highlights the potential for hardware implementations of neural networks with a weight change of memristor device with nonlinearity (NL) of potentiation and depression of 1.94 and 0.89, respectively, with an accuracy of 93%. The outcomes of this research contribute to the progress of next‐generation low‐power, self‐powered unidirectional neuromorphic perception networks with correlated learning and trainable memory capabilities.

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