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

Abstract

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.

Porous crystalline materials for memories and neuromorphic computing systems

“…, artificial synapses and artificial neurons) is crucial for constructing neuromorphic computing systems capable of overcoming the von Neumann bottleneck in this post-Moore's law era. 71,72,74,75,89,393,394,479,480 Up to now, various devices, including memristor, 70,72,73,481–488 flash memory, 285,489–492 EG-FET, 293,295,296,489,490,493–496 and memtransistor, 497–499 based on different functional materials, 484,500,501 such as 2D materials, 85–88,387,502–508 perovskite, 76–80,389,509,510 biomaterials, 81,82 TMO, 385,511–513 and organic materials, 71,90,514,515 have been utilized for neuromorphic devices.…”

“…Up to now, various devices, including memristor, 70,72,73,481-488 flash memory, 285,[489][490][491][492] EG-FET, 293,295,296,489,490,493-496 and memtransistor, [497][498][499] based on different functional materials, 484,500,501 such as 2D materials, 85-88,387,502-508 perovskite, 76-80,389,509,510 biomaterials, 81,82 TMO, 385,[511][512][513] and organic materials, 71,90,514,515 have been utilized for neuromorphic devices.…”

Polyoxometalate-Modified g-C3N4 Composites with High Work Function for Triboelectric Nanogenerators

“…With the advent of the era of 5G technology, the rapid development of emerging wearable flexible electronics and sensors has increased the demand for renewable, sustainable, and portable power supplies. − To power these numerous electronic devices, people are committed to developing portable power technologies, such as solar cells, , pyroelectric nanogenerators, , piezoelectric nanogenerators, , and triboelectric nanogenerators (TENG). , Among them, TENG is the one that has garnered the most interest among them all because of its ability to capture and transform mechanical vibration, sound waves, water movement, vehicle movement, and human activity in the surrounding environment into electric energy. , To meet the application requirements, various strategies have been proposed to improve the output performance of TENG, such as material design and structural optimization. − From the perspective of material design, it is very important to select the appropriate friction material. , At present, the most widely used friction material organic polymer has a relatively single form, especially in the functionalization and modification of certain difficulties. Therefore, it is necessary to develop new friction materials that are easy to modify and functionalize to improve the performance of TENG.…”

“…With the advent of the era of 5G technology, the rapid development of emerging wearable flexible electronics and sensors has increased the demand for renewable, sustainable, and portable power supplies. 1−3 To power these numerous electronic devices, people are committed to developing portable power technologies, such as solar cells, 4,5 pyroelectric nanogenerators, 6,7 piezoelectric nanogenerators, 8,9 and triboelectric nanogenerators (TENG). 10,11 Among them, TENG is the one that has garnered the most interest among them all because of its ability to capture and transform mechanical vibration, sound waves, water movement, vehicle movement, and human activity in the surrounding environment into electric energy.…”

Emulation of Pavlovian conditioning and pattern recognition through fully connected neural networks using Holmium oxide (Ho2O3) based synaptic RRAM device

“…Time-dependent LTP plays a vital role in mimicking the Pavlovian conditioning [8]. Various materials based synaptic resistive random access memory (RRAM) devices have been developed for neuromorphic applications such as oxides [9][10][11][12][13][14][15][16][17], chalcogenides [18,19], and 2D materials [20,21]. Despite all the above, the conductance obtained from synaptic RRAM devices can also be utilized in artificial neural networks (ANN's) for the recognition of image, video and audio data etc with higher accuracy [22].…”