Memristive artificial synapses based on Au–TiO2 composite thin film for neuromorphic computing
Neuromorphic computing architecture is considered to be a highly desirable next-generation computing architecture as it simulates the way the brain processes information. The basic device supporting such an architecture is called an artificial synapse, which possesses synapse-like functionalities. H...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2023-06-01
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| Series: | APL Materials |
| Online Access: | http://dx.doi.org/10.1063/5.0149154 |
| _version_ | 1797771787027611648 |
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| author | Haonan Zhu Zhenxun Tang Guoliang Wang Yuan Fang Jijie Huang Yue Zheng |
| author_facet | Haonan Zhu Zhenxun Tang Guoliang Wang Yuan Fang Jijie Huang Yue Zheng |
| author_sort | Haonan Zhu |
| collection | DOAJ |
| description | Neuromorphic computing architecture is considered to be a highly desirable next-generation computing architecture as it simulates the way the brain processes information. The basic device supporting such an architecture is called an artificial synapse, which possesses synapse-like functionalities. Here in this work, an Au–TiO2 composite thin film (Au nanoparticles embedding into TiO2 matrix) based memristive artificial synapse has been fabricated with excellent interface-type resistive switching (RS) characteristics. The conductivity of the device can be continuously tuned by applying different sequences of pulses, which could be analogous to the weight change of synapses. Various synaptic behaviors have been emulated, such as long-term potentiation/depression, short-term/long-term memory, learning-forgetting process, and paired-pulse facilitation. Finally, an artificial neural network for hand-written digits recognition has been constructed with an accuracy level as high as ∼90%. The excellent performance of the Au–TiO2 based device demonstrates the availability of incorporating the second phase to tune RS properties and shows its potential in a memristor for artificial synapses and neuromorphic computing with enhanced performance. |
| first_indexed | 2024-03-12T21:42:42Z |
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| id | doaj.art-bc8a47275aa143f092b5031a5529c64f |
| institution | Directory Open Access Journal |
| issn | 2166-532X |
| language | English |
| last_indexed | 2024-03-12T21:42:42Z |
| publishDate | 2023-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Materials |
| spelling | doaj.art-bc8a47275aa143f092b5031a5529c64f2023-07-26T16:22:28ZengAIP Publishing LLCAPL Materials2166-532X2023-06-01116061103061103-910.1063/5.0149154Memristive artificial synapses based on Au–TiO2 composite thin film for neuromorphic computingHaonan Zhu0Zhenxun Tang1Guoliang Wang2Yuan Fang3Jijie Huang4Yue Zheng5School of Physics, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Physics, Sun Yat-sen University, Guangzhou 510275, ChinaGuangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, Sun Yat-sen University, Guangzhou 510275, ChinaGuangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, Sun Yat-sen University, Guangzhou 510275, ChinaGuangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Physics, Sun Yat-sen University, Guangzhou 510275, ChinaNeuromorphic computing architecture is considered to be a highly desirable next-generation computing architecture as it simulates the way the brain processes information. The basic device supporting such an architecture is called an artificial synapse, which possesses synapse-like functionalities. Here in this work, an Au–TiO2 composite thin film (Au nanoparticles embedding into TiO2 matrix) based memristive artificial synapse has been fabricated with excellent interface-type resistive switching (RS) characteristics. The conductivity of the device can be continuously tuned by applying different sequences of pulses, which could be analogous to the weight change of synapses. Various synaptic behaviors have been emulated, such as long-term potentiation/depression, short-term/long-term memory, learning-forgetting process, and paired-pulse facilitation. Finally, an artificial neural network for hand-written digits recognition has been constructed with an accuracy level as high as ∼90%. The excellent performance of the Au–TiO2 based device demonstrates the availability of incorporating the second phase to tune RS properties and shows its potential in a memristor for artificial synapses and neuromorphic computing with enhanced performance.http://dx.doi.org/10.1063/5.0149154 |
| spellingShingle | Haonan Zhu Zhenxun Tang Guoliang Wang Yuan Fang Jijie Huang Yue Zheng Memristive artificial synapses based on Au–TiO2 composite thin film for neuromorphic computing APL Materials |
| title | Memristive artificial synapses based on Au–TiO2 composite thin film for neuromorphic computing |
| title_full | Memristive artificial synapses based on Au–TiO2 composite thin film for neuromorphic computing |
| title_fullStr | Memristive artificial synapses based on Au–TiO2 composite thin film for neuromorphic computing |
| title_full_unstemmed | Memristive artificial synapses based on Au–TiO2 composite thin film for neuromorphic computing |
| title_short | Memristive artificial synapses based on Au–TiO2 composite thin film for neuromorphic computing |
| title_sort | memristive artificial synapses based on au tio2 composite thin film for neuromorphic computing |
| url | http://dx.doi.org/10.1063/5.0149154 |
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