Non-Volatile Reconfigurable Optical Digital Diffractive Neural Network Based on Phase Change Material
Optical diffractive neural networks have sparked extensive research due to their low power consumption and high-speed capabilities in image processing. Here we propose and design a reconfigurable all-optical diffractive neural network structure with digital non-volatile optical neurons. The optical...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10770557/ |
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| author | Qiaomu Hu Jingyu Zhao Chu Wu Rui Zeng Xiaobing Zhou Shuang Zheng Minming Zhang |
| author_facet | Qiaomu Hu Jingyu Zhao Chu Wu Rui Zeng Xiaobing Zhou Shuang Zheng Minming Zhang |
| author_sort | Qiaomu Hu |
| collection | DOAJ |
| description | Optical diffractive neural networks have sparked extensive research due to their low power consumption and high-speed capabilities in image processing. Here we propose and design a reconfigurable all-optical diffractive neural network structure with digital non-volatile optical neurons. The optical neurons are built with Sb<sub>2</sub>Se<sub>3</sub> phase-change material and can switch between crystalline and amorphous states with no constant energy supply. Using three reconfigurable non-volatile digital diffractive layers and 10 photodetectors connected to a reconfigurable resistor network, our model achieves an accuracy of 94.46% in the handwritten digit recognition task. Moreover, the fabrication and assembly robustness of the proposed optical diffractive neural network is verified through full-vector diffractive simulation. Thanks to its reconfigurability and low energy supply, the digital optical diffractive neural network holds great potential to facilitate a programmable and low-power-consumption photonic processor for optical-artificial-intelligence. |
| format | Article |
| id | doaj-art-bf62840f65b44ea5a237d1d9403bee1a |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-bf62840f65b44ea5a237d1d9403bee1a2025-01-24T00:00:35ZengIEEEIEEE Photonics Journal1943-06552024-01-011661810.1109/JPHOT.2024.350805210770557Non-Volatile Reconfigurable Optical Digital Diffractive Neural Network Based on Phase Change MaterialQiaomu Hu0https://orcid.org/0000-0001-6375-6161Jingyu Zhao1https://orcid.org/0009-0003-8790-671XChu Wu2Rui Zeng3Xiaobing Zhou4https://orcid.org/0009-0000-6599-3822Shuang Zheng5https://orcid.org/0000-0002-4825-5163Minming Zhang6https://orcid.org/0000-0002-3742-1445School of Optical and Electronic Information, Wuhan, ChinaSchool of Optical and Electronic Information, Wuhan, ChinaSchool of Optical and Electronic Information, Wuhan, ChinaSchool of Optical and Electronic Information, Wuhan, ChinaSchool of Optical and Electronic Information, Wuhan, ChinaSchool of Optical and Electronic Information, Wuhan, ChinaSchool of Optical and Electronic Information, Wuhan, ChinaOptical diffractive neural networks have sparked extensive research due to their low power consumption and high-speed capabilities in image processing. Here we propose and design a reconfigurable all-optical diffractive neural network structure with digital non-volatile optical neurons. The optical neurons are built with Sb<sub>2</sub>Se<sub>3</sub> phase-change material and can switch between crystalline and amorphous states with no constant energy supply. Using three reconfigurable non-volatile digital diffractive layers and 10 photodetectors connected to a reconfigurable resistor network, our model achieves an accuracy of 94.46% in the handwritten digit recognition task. Moreover, the fabrication and assembly robustness of the proposed optical diffractive neural network is verified through full-vector diffractive simulation. Thanks to its reconfigurability and low energy supply, the digital optical diffractive neural network holds great potential to facilitate a programmable and low-power-consumption photonic processor for optical-artificial-intelligence.https://ieeexplore.ieee.org/document/10770557/Optical neural networksilicon photonicsphase change material |
| spellingShingle | Qiaomu Hu Jingyu Zhao Chu Wu Rui Zeng Xiaobing Zhou Shuang Zheng Minming Zhang Non-Volatile Reconfigurable Optical Digital Diffractive Neural Network Based on Phase Change Material IEEE Photonics Journal Optical neural network silicon photonics phase change material |
| title | Non-Volatile Reconfigurable Optical Digital Diffractive Neural Network Based on Phase Change Material |
| title_full | Non-Volatile Reconfigurable Optical Digital Diffractive Neural Network Based on Phase Change Material |
| title_fullStr | Non-Volatile Reconfigurable Optical Digital Diffractive Neural Network Based on Phase Change Material |
| title_full_unstemmed | Non-Volatile Reconfigurable Optical Digital Diffractive Neural Network Based on Phase Change Material |
| title_short | Non-Volatile Reconfigurable Optical Digital Diffractive Neural Network Based on Phase Change Material |
| title_sort | non volatile reconfigurable optical digital diffractive neural network based on phase change material |
| topic | Optical neural network silicon photonics phase change material |
| url | https://ieeexplore.ieee.org/document/10770557/ |
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