All-optical combinational logical units featuring fifth-order cascade
Modern computational technologies are gradually encountering significant limitations, driving a shift toward alternative paradigms such as optical computing. In this study, novel all-optical combinational logic units based on diffractive neural networks (D2NNs) were introduced, which were designed t...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Chip |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2709472324000303 |
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| author | Haiqi Gao Yu Shao Yipeng Chen Junren Wen Yuchuan Shao Yueguang Zhang Weidong Shen Chenying Yang |
| author_facet | Haiqi Gao Yu Shao Yipeng Chen Junren Wen Yuchuan Shao Yueguang Zhang Weidong Shen Chenying Yang |
| author_sort | Haiqi Gao |
| collection | DOAJ |
| description | Modern computational technologies are gradually encountering significant limitations, driving a shift toward alternative paradigms such as optical computing. In this study, novel all-optical combinational logic units based on diffractive neural networks (D2NNs) were introduced, which were designed to perform high-order logical operations efficiently and swiftly with the adoption of only two modulation layers. This innovative design exhibits increased processing speed, improved energy efficiency, robust environmental stability, and high error tolerance, making it exceptionally well-suited for a broad spectrum of applications in optical computing and communications. By leveraging the transfer learning, we successfully developed a fifth-order cascaded combinational logic circuit for a practical information transmission system. Furthermore, we revealed a pioneering application of the device in optical time division multiplexing (OTDM), demonstrating its capability to manage high-speed data transfer seamlessly without the need for electronic conversion. Extensive simulations and experimental validations demonstrate the potential of the model as a foundational technology for future optical computing architectures, which paves the way toward more sustainable and efficient optical data processing platforms. |
| format | Article |
| id | doaj-art-d85614a9e4a34d2c93daa62ce41d2b4d |
| institution | DOAJ |
| issn | 2709-4723 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Chip |
| spelling | doaj-art-d85614a9e4a34d2c93daa62ce41d2b4d2025-08-20T02:50:55ZengElsevierChip2709-47232024-12-013410011210.1016/j.chip.2024.100112All-optical combinational logical units featuring fifth-order cascadeHaiqi Gao0Yu Shao1Yipeng Chen2Junren Wen3Yuchuan Shao4Yueguang Zhang5Weidong Shen6Chenying Yang7Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, ChinaHangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, ChinaHangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, ChinaHangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, ChinaHangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, ChinaState key Laboratory of Modern Optical Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou 310027, ChinaState key Laboratory of Modern Optical Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou 310027, China; Corresponding authors.Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; State key Laboratory of Modern Optical Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou 310027, China; Corresponding authors.Modern computational technologies are gradually encountering significant limitations, driving a shift toward alternative paradigms such as optical computing. In this study, novel all-optical combinational logic units based on diffractive neural networks (D2NNs) were introduced, which were designed to perform high-order logical operations efficiently and swiftly with the adoption of only two modulation layers. This innovative design exhibits increased processing speed, improved energy efficiency, robust environmental stability, and high error tolerance, making it exceptionally well-suited for a broad spectrum of applications in optical computing and communications. By leveraging the transfer learning, we successfully developed a fifth-order cascaded combinational logic circuit for a practical information transmission system. Furthermore, we revealed a pioneering application of the device in optical time division multiplexing (OTDM), demonstrating its capability to manage high-speed data transfer seamlessly without the need for electronic conversion. Extensive simulations and experimental validations demonstrate the potential of the model as a foundational technology for future optical computing architectures, which paves the way toward more sustainable and efficient optical data processing platforms.http://www.sciencedirect.com/science/article/pii/S2709472324000303Diffractive neural networksLogical operationsOptical time division multiplexingOptical computing |
| spellingShingle | Haiqi Gao Yu Shao Yipeng Chen Junren Wen Yuchuan Shao Yueguang Zhang Weidong Shen Chenying Yang All-optical combinational logical units featuring fifth-order cascade Chip Diffractive neural networks Logical operations Optical time division multiplexing Optical computing |
| title | All-optical combinational logical units featuring fifth-order cascade |
| title_full | All-optical combinational logical units featuring fifth-order cascade |
| title_fullStr | All-optical combinational logical units featuring fifth-order cascade |
| title_full_unstemmed | All-optical combinational logical units featuring fifth-order cascade |
| title_short | All-optical combinational logical units featuring fifth-order cascade |
| title_sort | all optical combinational logical units featuring fifth order cascade |
| topic | Diffractive neural networks Logical operations Optical time division multiplexing Optical computing |
| url | http://www.sciencedirect.com/science/article/pii/S2709472324000303 |
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