Deep Learning for Polarization Optical System Automated Design
Aiming at the problem that traditional design methods make it difficult to control the polarization aberration distribution of optical systems quickly and accurately, this study proposes an automatic optimization design method for polarization optical systems based on deep learning. The unsupervised...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/11/2/164 |
| _version_ | 1827342933349629952 |
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| author | Haodong Shi Ruihan Fan Chunfeng He Jiayu Wang Shuai Yang Miao Xu Hongyu Sun Yingchao Li Qiang Fu |
| author_facet | Haodong Shi Ruihan Fan Chunfeng He Jiayu Wang Shuai Yang Miao Xu Hongyu Sun Yingchao Li Qiang Fu |
| author_sort | Haodong Shi |
| collection | DOAJ |
| description | Aiming at the problem that traditional design methods make it difficult to control the polarization aberration distribution of optical systems quickly and accurately, this study proposes an automatic optimization design method for polarization optical systems based on deep learning. The unsupervised training model based on ray tracing and polarized ray tracing was constructed by learning the reference lens structural feature data from the optical lens library, and the generalization ability of the deep neural network model was improved to achieve the automatic optimization design of the polarized optical system. The design results show that the optical system structure optimized by the network model is close to the reference lens in the full field of view and the full spectrum and that the optical system structure can be designed for different focal length requirements. The success rate of 1 million groups of initial structures designed is better than 96.403%, and the polarization effect of the optical system is effectively controlled. The proposed deep learning approach to optical design provides a new solution for future complex optical systems and also provides an effective way to improve the design accuracy of special optical systems such as polarization optical systems. |
| first_indexed | 2024-03-07T22:16:56Z |
| format | Article |
| id | doaj.art-61102133334c43be87baa53b1bf027fb |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-07T22:16:56Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-61102133334c43be87baa53b1bf027fb2024-02-23T15:31:42ZengMDPI AGPhotonics2304-67322024-02-0111216410.3390/photonics11020164Deep Learning for Polarization Optical System Automated DesignHaodong Shi0Ruihan Fan1Chunfeng He2Jiayu Wang3Shuai Yang4Miao Xu5Hongyu Sun6Yingchao Li7Qiang Fu8Jilin Provincial Key Laboratory of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun 130022, ChinaJilin Provincial Key Laboratory of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun 130022, ChinaJilin Provincial Key Laboratory of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun 130022, ChinaJilin Provincial Key Laboratory of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun 130022, ChinaJilin Provincial Key Laboratory of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun 130022, ChinaJilin Provincial Key Laboratory of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun 130022, ChinaJilin Provincial Key Laboratory of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun 130022, ChinaJilin Provincial Key Laboratory of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun 130022, ChinaJilin Provincial Key Laboratory of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun 130022, ChinaAiming at the problem that traditional design methods make it difficult to control the polarization aberration distribution of optical systems quickly and accurately, this study proposes an automatic optimization design method for polarization optical systems based on deep learning. The unsupervised training model based on ray tracing and polarized ray tracing was constructed by learning the reference lens structural feature data from the optical lens library, and the generalization ability of the deep neural network model was improved to achieve the automatic optimization design of the polarized optical system. The design results show that the optical system structure optimized by the network model is close to the reference lens in the full field of view and the full spectrum and that the optical system structure can be designed for different focal length requirements. The success rate of 1 million groups of initial structures designed is better than 96.403%, and the polarization effect of the optical system is effectively controlled. The proposed deep learning approach to optical design provides a new solution for future complex optical systems and also provides an effective way to improve the design accuracy of special optical systems such as polarization optical systems.https://www.mdpi.com/2304-6732/11/2/164deep learningpolarization aberrationautomatic optimizationray tracing |
| spellingShingle | Haodong Shi Ruihan Fan Chunfeng He Jiayu Wang Shuai Yang Miao Xu Hongyu Sun Yingchao Li Qiang Fu Deep Learning for Polarization Optical System Automated Design Photonics deep learning polarization aberration automatic optimization ray tracing |
| title | Deep Learning for Polarization Optical System Automated Design |
| title_full | Deep Learning for Polarization Optical System Automated Design |
| title_fullStr | Deep Learning for Polarization Optical System Automated Design |
| title_full_unstemmed | Deep Learning for Polarization Optical System Automated Design |
| title_short | Deep Learning for Polarization Optical System Automated Design |
| title_sort | deep learning for polarization optical system automated design |
| topic | deep learning polarization aberration automatic optimization ray tracing |
| url | https://www.mdpi.com/2304-6732/11/2/164 |
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