All-optical Fourier neural network using partially coherent light
Optical neural networks present distinct advantages over traditional electrical counterparts, such as accelerated data processing and reduced energy consumption. While coherent light is conventionally used in optical neural networks, our study proposed harnessing spatially incoherent light in all-op...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Chip |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2709472325000140 |
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| author | Jianwei Qin Yanbing Liu Yan Liu Xun Liu Wei Li Fangwei Ye |
| author_facet | Jianwei Qin Yanbing Liu Yan Liu Xun Liu Wei Li Fangwei Ye |
| author_sort | Jianwei Qin |
| collection | DOAJ |
| description | Optical neural networks present distinct advantages over traditional electrical counterparts, such as accelerated data processing and reduced energy consumption. While coherent light is conventionally used in optical neural networks, our study proposed harnessing spatially incoherent light in all-optical Fourier neural networks. Contrary to natural predictions of declining target recognition accuracy with increased incoherence, our experimental results demonstrated a surprising outcome: improved accuracy with incoherent light. We attribute this enhancement to spatially incoherent light's ability to alleviate experimental errors like diffraction rings and laser speckle. Our experiments introduced controllable spatial incoherence by passing monochromatic light through a spatial light modulator featuring a dynamically changing random phase array. These findings underscore partially coherent light's potential to optimize optical neural networks, delivering dependable and efficient solutions for applications demanding consistent accuracy and robustness across diverse conditions, including on-chip optical computing, photonic interconnects, and reconfigurable optical processors. |
| format | Article |
| id | doaj-art-35fa423394c648e39daa24bc7c6d096f |
| institution | Kabale University |
| issn | 2709-4723 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Chip |
| spelling | doaj-art-35fa423394c648e39daa24bc7c6d096f2025-08-20T03:50:01ZengElsevierChip2709-47232025-09-014310014010.1016/j.chip.2025.100140All-optical Fourier neural network using partially coherent lightJianwei Qin0Yanbing Liu1Yan Liu2Xun Liu3Wei Li4Fangwei Ye5School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, ChinaBeijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, ChinaBeijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, China; Corresponding authors.School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China; School of Physics, Chengdu University of Technology, Chengdu 610059, China; Corresponding authors.Optical neural networks present distinct advantages over traditional electrical counterparts, such as accelerated data processing and reduced energy consumption. While coherent light is conventionally used in optical neural networks, our study proposed harnessing spatially incoherent light in all-optical Fourier neural networks. Contrary to natural predictions of declining target recognition accuracy with increased incoherence, our experimental results demonstrated a surprising outcome: improved accuracy with incoherent light. We attribute this enhancement to spatially incoherent light's ability to alleviate experimental errors like diffraction rings and laser speckle. Our experiments introduced controllable spatial incoherence by passing monochromatic light through a spatial light modulator featuring a dynamically changing random phase array. These findings underscore partially coherent light's potential to optimize optical neural networks, delivering dependable and efficient solutions for applications demanding consistent accuracy and robustness across diverse conditions, including on-chip optical computing, photonic interconnects, and reconfigurable optical processors.http://www.sciencedirect.com/science/article/pii/S2709472325000140Partially coherent lightOptical neural network |
| spellingShingle | Jianwei Qin Yanbing Liu Yan Liu Xun Liu Wei Li Fangwei Ye All-optical Fourier neural network using partially coherent light Chip Partially coherent light Optical neural network |
| title | All-optical Fourier neural network using partially coherent light |
| title_full | All-optical Fourier neural network using partially coherent light |
| title_fullStr | All-optical Fourier neural network using partially coherent light |
| title_full_unstemmed | All-optical Fourier neural network using partially coherent light |
| title_short | All-optical Fourier neural network using partially coherent light |
| title_sort | all optical fourier neural network using partially coherent light |
| topic | Partially coherent light Optical neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2709472325000140 |
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