Biodegradable and Flexible Polymer‐Based Memristor Possessing Optimized Synaptic Plasticity for Eco‐Friendly Wearable Neural Networks with High Energy Efficiency
Organic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco‐friendly flexible neural networks. However, in the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-05-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202200272 |
| _version_ | 1797823373595639808 |
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| author | Sungjun Oh Hyungjin Kim Seong Eun Kim Min-Hwi Kim Hea-Lim Park Sin-Hyung Lee |
| author_facet | Sungjun Oh Hyungjin Kim Seong Eun Kim Min-Hwi Kim Hea-Lim Park Sin-Hyung Lee |
| author_sort | Sungjun Oh |
| collection | DOAJ |
| description | Organic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco‐friendly flexible neural networks. However, in the transient neural networks, achieving flexible memristors with biorealistic synaptic plasticity for energy efficient learning processes is still challenging. Herein, a biodegradable and flexible polymer‐based memristor, suitable for the spike‐dependent learning process, is demonstrated. An electrochemical metallization phenomenon for the conductive nanofilament growth in a polymer medium of poly (vinyl alcohol) (PVA) is analyzed and a PVA‐based transient and flexible artificial synapse is developed. The developed device exhibits superior biodegradability and stable mechanical flexibility due to the high water solubility and excellent tensile strength of the PVA film, respectively. In addition, the developed flexible memristor is operated as a reliable synaptic device with optimized synaptic plasticity, which is ideal for artificial neural networks with the spike‐dependent operations. The developed device is found to be effectively served as a reliable synaptic component with high energy efficiency in practical neural networks. This novel strategy for developing transient and flexible artificial synapses can be a fundamental platform for realizing eco‐friendly wearable intelligent systems. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166603245.58711630/v1. |
| first_indexed | 2024-03-13T10:23:04Z |
| format | Article |
| id | doaj.art-ac2c38f7fb0c4b0b8ce0b6e18949cbaf |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-03-13T10:23:04Z |
| publishDate | 2023-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-ac2c38f7fb0c4b0b8ce0b6e18949cbaf2023-05-20T03:54:51ZengWileyAdvanced Intelligent Systems2640-45672023-05-0155n/an/a10.1002/aisy.202200272Biodegradable and Flexible Polymer‐Based Memristor Possessing Optimized Synaptic Plasticity for Eco‐Friendly Wearable Neural Networks with High Energy EfficiencySungjun Oh0Hyungjin Kim1Seong Eun Kim2Min-Hwi Kim3Hea-Lim Park4Sin-Hyung Lee5School of Electronics Engineering Kyungpook National University 80 Daehak-ro Buk-gu Daegu 702-701 Republic of KoreaDepartment of Materials Science and Engineering Yonsei University Seoul 03722 Republic of KoreaSchool of Electronics Engineering Kyungpook National University 80 Daehak-ro Buk-gu Daegu 702-701 Republic of KoreaSchool of Electrical and Electronics Engineering Chung-Ang University Seoul 06974 Republic of KoreaDepartment of Materials Science and Engineering Seoul National University of Science and Technology Seoul 01811 Republic of KoreaSchool of Electronics Engineering Kyungpook National University 80 Daehak-ro Buk-gu Daegu 702-701 Republic of KoreaOrganic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco‐friendly flexible neural networks. However, in the transient neural networks, achieving flexible memristors with biorealistic synaptic plasticity for energy efficient learning processes is still challenging. Herein, a biodegradable and flexible polymer‐based memristor, suitable for the spike‐dependent learning process, is demonstrated. An electrochemical metallization phenomenon for the conductive nanofilament growth in a polymer medium of poly (vinyl alcohol) (PVA) is analyzed and a PVA‐based transient and flexible artificial synapse is developed. The developed device exhibits superior biodegradability and stable mechanical flexibility due to the high water solubility and excellent tensile strength of the PVA film, respectively. In addition, the developed flexible memristor is operated as a reliable synaptic device with optimized synaptic plasticity, which is ideal for artificial neural networks with the spike‐dependent operations. The developed device is found to be effectively served as a reliable synaptic component with high energy efficiency in practical neural networks. This novel strategy for developing transient and flexible artificial synapses can be a fundamental platform for realizing eco‐friendly wearable intelligent systems. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166603245.58711630/v1.https://doi.org/10.1002/aisy.202200272artificial synapsesflexible memristorsneural networkssynaptic functiontransient memristors |
| spellingShingle | Sungjun Oh Hyungjin Kim Seong Eun Kim Min-Hwi Kim Hea-Lim Park Sin-Hyung Lee Biodegradable and Flexible Polymer‐Based Memristor Possessing Optimized Synaptic Plasticity for Eco‐Friendly Wearable Neural Networks with High Energy Efficiency Advanced Intelligent Systems artificial synapses flexible memristors neural networks synaptic function transient memristors |
| title | Biodegradable and Flexible Polymer‐Based Memristor Possessing Optimized Synaptic Plasticity for Eco‐Friendly Wearable Neural Networks with High Energy Efficiency |
| title_full | Biodegradable and Flexible Polymer‐Based Memristor Possessing Optimized Synaptic Plasticity for Eco‐Friendly Wearable Neural Networks with High Energy Efficiency |
| title_fullStr | Biodegradable and Flexible Polymer‐Based Memristor Possessing Optimized Synaptic Plasticity for Eco‐Friendly Wearable Neural Networks with High Energy Efficiency |
| title_full_unstemmed | Biodegradable and Flexible Polymer‐Based Memristor Possessing Optimized Synaptic Plasticity for Eco‐Friendly Wearable Neural Networks with High Energy Efficiency |
| title_short | Biodegradable and Flexible Polymer‐Based Memristor Possessing Optimized Synaptic Plasticity for Eco‐Friendly Wearable Neural Networks with High Energy Efficiency |
| title_sort | biodegradable and flexible polymer based memristor possessing optimized synaptic plasticity for eco friendly wearable neural networks with high energy efficiency |
| topic | artificial synapses flexible memristors neural networks synaptic function transient memristors |
| url | https://doi.org/10.1002/aisy.202200272 |
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