Large-scale high uniform optoelectronic synapses array for artificial visual neural network
Abstract Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic device...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025-01-01
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| Series: | Microsystems & Nanoengineering |
| Online Access: | https://doi.org/10.1038/s41378-024-00859-2 |
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| author | Fanqing Zhang Chunyang Li Zhicheng Chen Haiqiu Tan Zhongyi Li Chengzhai Lv Shuai Xiao Lining Wu Jing Zhao |
| author_facet | Fanqing Zhang Chunyang Li Zhicheng Chen Haiqiu Tan Zhongyi Li Chengzhai Lv Shuai Xiao Lining Wu Jing Zhao |
| author_sort | Fanqing Zhang |
| collection | DOAJ |
| description | Abstract Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic devices array for the artificial visual neuromorphic system, within the size of 0.7 × 0.7 cm2, which integrates sensing, memory, and processing functions. The highly uniform floating-gate synaptic transistors array were constructed by the wafer-scale grown monolayer molybdenum disulfide with Au nanoparticles (NPs) acting as the electrons capture layers. Various synaptic plasticity behaviors have been achieved owing to the switchable electronic storage performance. The excellent optical/electrical coordination capabilities were implemented by paralleled processing both the optical and electrical signals the synaptic array of 784 devices, enabling to realize the badges and letters writing and erasing process. Finally, the established artificial visual convolutional neural network (CNN) through optical/electrical signal modulation can reach the high digit recognition accuracy of 96.5%. Therefore, our results provide a feasible route for future large-scale integrated artificial visual neuromorphic system. |
| format | Article |
| id | doaj-art-027ed3a3379a45ddab7d9ce9e4181c9e |
| institution | Kabale University |
| issn | 2055-7434 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Microsystems & Nanoengineering |
| spelling | doaj-art-027ed3a3379a45ddab7d9ce9e4181c9e2025-01-19T12:27:05ZengNature Publishing GroupMicrosystems & Nanoengineering2055-74342025-01-0111111010.1038/s41378-024-00859-2Large-scale high uniform optoelectronic synapses array for artificial visual neural networkFanqing Zhang0Chunyang Li1Zhicheng Chen2Haiqiu Tan3Zhongyi Li4Chengzhai Lv5Shuai Xiao6Lining Wu7Jing Zhao8State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of EducationState Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of EducationLaser Micro/Nano Fabrication Laboratory, School of Mechanical Engineering, Beijing Institute of TechnologySchool of Mechanical Engineering, Beijing Institute of TechnologyState Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of EducationState Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of EducationState Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of EducationState Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of EducationState Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of EducationAbstract Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic devices array for the artificial visual neuromorphic system, within the size of 0.7 × 0.7 cm2, which integrates sensing, memory, and processing functions. The highly uniform floating-gate synaptic transistors array were constructed by the wafer-scale grown monolayer molybdenum disulfide with Au nanoparticles (NPs) acting as the electrons capture layers. Various synaptic plasticity behaviors have been achieved owing to the switchable electronic storage performance. The excellent optical/electrical coordination capabilities were implemented by paralleled processing both the optical and electrical signals the synaptic array of 784 devices, enabling to realize the badges and letters writing and erasing process. Finally, the established artificial visual convolutional neural network (CNN) through optical/electrical signal modulation can reach the high digit recognition accuracy of 96.5%. Therefore, our results provide a feasible route for future large-scale integrated artificial visual neuromorphic system.https://doi.org/10.1038/s41378-024-00859-2 |
| spellingShingle | Fanqing Zhang Chunyang Li Zhicheng Chen Haiqiu Tan Zhongyi Li Chengzhai Lv Shuai Xiao Lining Wu Jing Zhao Large-scale high uniform optoelectronic synapses array for artificial visual neural network Microsystems & Nanoengineering |
| title | Large-scale high uniform optoelectronic synapses array for artificial visual neural network |
| title_full | Large-scale high uniform optoelectronic synapses array for artificial visual neural network |
| title_fullStr | Large-scale high uniform optoelectronic synapses array for artificial visual neural network |
| title_full_unstemmed | Large-scale high uniform optoelectronic synapses array for artificial visual neural network |
| title_short | Large-scale high uniform optoelectronic synapses array for artificial visual neural network |
| title_sort | large scale high uniform optoelectronic synapses array for artificial visual neural network |
| url | https://doi.org/10.1038/s41378-024-00859-2 |
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