| 基于全局拓扑优化深度学习模型的超构光栅分束器 |
| |
| 引用本文： | 邓人隽,史坦,李向平,邓子岚.基于全局拓扑优化深度学习模型的超构光栅分束器[J].红外与激光工程,2021,50(5):20211028-1-20211028-4. |
| |
| 作者姓名： | 邓人隽 史坦 李向平 邓子岚 |
| |
| 作者单位： | 暨南大学 光子技术研究院 广东省光纤传感与通信技术重点实验室，广东 广州 510632 |
| |
| 基金项目： | 国家自然科学基金面上项目(62075084)；广东省珠江人才创新团队项目(2016ZT06D081)；广东省科学基金面上项目(2020A1515010615)；广州市基础与应用基础研究项目(202102020566)；中央高校基本业务费(21620415) |
| |
| 摘 要： | 将深度学习模型应用于超构光栅分束器的逆向设计，可以在全局范围内获得具有良好均匀性和高衍射效率的结构。利用基于全局拓扑优化的深度学习模型，围绕超构光栅分束器的结构设计和衍射效率及均匀性等光学性能展开了一系列的研究。在波长为900 nm的入射光下，基于全局拓扑优化深度学习模型设计出大角度高衍射效率超构光栅分束器，设计的分束角为120°与150°时衍射效率分别达到95%与85%。
|
| 关 键 词： | 超构光栅 分束器 深度学习 |
| 收稿时间： | 2021-04-01 |
Global topology optimized metagrating beam splitter based on deep learning |
| |
| Affiliation: | Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510632, China |
| |
| Abstract: | With the help of the deep learning model applied in the inverse design of the metagrating beam splitter, good uniformity and high diffraction efficiency can be obtained. The structure design, diffraction efficiency and uniformity of the metagrating beam splitter was studied by using the global topology optimization neural networks. Under the working wavelength of 900 nm, the beam splitter with splitting angle of 120° and 150° designed based on the global topology optimization networks had high diffraction efficiencies of 95% for 120° and 85% for 150°. |
| |
| Keywords: | |
|
| 点击此处可从《红外与激光工程》浏览原始摘要信息 |
|
点击此处可从《红外与激光工程》下载全文 |
