Direct laser writing of graphene oxide for ultra-low power consumption memristors in reservoir computing for digital recognition
A memristor is a promising candidate of new electronic synaptic devices for neuromorphic computing. However, conventional memristors often exhibit complex device structures, cumbersome manufacturing processes, and high energy consumption. Graphene-based materials show great potential as the building...
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| Format: | Article |
| Language: | English |
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Science Press
2022-08-01
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| Series: | National Science Open |
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| Online Access: | https://www.sciengine.com/doi/10.1360/nso/20220020 |
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| author | Chen Min Wan Zhengfen Dong Hao Chen Qinyu Gu Min Zhang Qiming |
| author_facet | Chen Min Wan Zhengfen Dong Hao Chen Qinyu Gu Min Zhang Qiming |
| author_sort | Chen Min |
| collection | DOAJ |
| description | A memristor is a promising candidate of new electronic synaptic devices for neuromorphic computing. However, conventional memristors often exhibit complex device structures, cumbersome manufacturing processes, and high energy consumption. Graphene-based materials show great potential as the building materials of memristors. With direct laser writing technology, this paper proposes a lateral memristor with reduced graphene oxide (rGO) and Pt as electrodes and graphene oxide (GO) as function material. This Pt/GO/rGO memristor with a facile lateral structure can be easily fabricated and demonstrates an ultra-low energy consumption of 200 nW. Typical synaptic behaviors are successfully emulated. Meanwhile, the Pt/GO/rGO memristor array is applied in the reservoir computing network, performing the digital recognition with a high accuracy of 95.74%. This work provides a simple and low-cost preparation method for the massive production of artificial synapses with low energy consumption, which will greatly facilitate the development of neural network computing hardware platforms. |
| format | Article |
| id | doaj-art-82a0159ad0eb48479c99bb0403a4528e |
| institution | DOAJ |
| issn | 2097-1168 |
| language | English |
| publishDate | 2022-08-01 |
| publisher | Science Press |
| record_format | Article |
| series | National Science Open |
| spelling | doaj-art-82a0159ad0eb48479c99bb0403a4528e2025-08-20T03:15:27ZengScience PressNational Science Open2097-11682022-08-01110.1360/nso/20220020eb33e642Direct laser writing of graphene oxide for ultra-low power consumption memristors in reservoir computing for digital recognitionChen Min0Wan Zhengfen1Dong Hao2Chen Qinyu3Gu Min4Zhang Qiming5["Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200090, China","Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200090, China"]["Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200090, China","Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200090, China"]["Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200090, China","Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200090, China"]["Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200090, China","Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200090, China"]["Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200090, China","Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200090, China"]["Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200090, China","Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200090, China"]A memristor is a promising candidate of new electronic synaptic devices for neuromorphic computing. However, conventional memristors often exhibit complex device structures, cumbersome manufacturing processes, and high energy consumption. Graphene-based materials show great potential as the building materials of memristors. With direct laser writing technology, this paper proposes a lateral memristor with reduced graphene oxide (rGO) and Pt as electrodes and graphene oxide (GO) as function material. This Pt/GO/rGO memristor with a facile lateral structure can be easily fabricated and demonstrates an ultra-low energy consumption of 200 nW. Typical synaptic behaviors are successfully emulated. Meanwhile, the Pt/GO/rGO memristor array is applied in the reservoir computing network, performing the digital recognition with a high accuracy of 95.74%. This work provides a simple and low-cost preparation method for the massive production of artificial synapses with low energy consumption, which will greatly facilitate the development of neural network computing hardware platforms.https://www.sciengine.com/doi/10.1360/nso/20220020direct laser writingmemristor arraygraphene oxidereservoir computing |
| spellingShingle | Chen Min Wan Zhengfen Dong Hao Chen Qinyu Gu Min Zhang Qiming Direct laser writing of graphene oxide for ultra-low power consumption memristors in reservoir computing for digital recognition National Science Open direct laser writing memristor array graphene oxide reservoir computing |
| title | Direct laser writing of graphene oxide for ultra-low power consumption memristors in reservoir computing for digital recognition |
| title_full | Direct laser writing of graphene oxide for ultra-low power consumption memristors in reservoir computing for digital recognition |
| title_fullStr | Direct laser writing of graphene oxide for ultra-low power consumption memristors in reservoir computing for digital recognition |
| title_full_unstemmed | Direct laser writing of graphene oxide for ultra-low power consumption memristors in reservoir computing for digital recognition |
| title_short | Direct laser writing of graphene oxide for ultra-low power consumption memristors in reservoir computing for digital recognition |
| title_sort | direct laser writing of graphene oxide for ultra low power consumption memristors in reservoir computing for digital recognition |
| topic | direct laser writing memristor array graphene oxide reservoir computing |
| url | https://www.sciengine.com/doi/10.1360/nso/20220020 |
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