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

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 & PLUSMN;4 V with a low operating current in a range of 1.4 & mu;A. 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.

Automated summary

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 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.