(Peer-Reviewed) Deep learning enables temperature-robust spectrometer with high resolution
Jiaan Gan, Mengyan Shen, Xin Xiao, Jinpeng Nong 农金鹏, Fu Feng 冯甫
Nanophononics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Shenzhen University, Shenzhen, 518060, China
中国 深圳 深圳大学 深圳市微尺度光信息技术重点实验室 纳米光子学研究中心
Abstract
Traditional multi-mode fiber spectrometers rely on algorithms to reconstruct the transmission matrix of the fiber, facing the challenge that the same wavelength can lead to many totally de-correlated speckle patterns as the transfer matrix changes rapidly with environment fluctuations (typically temperature fluctuation).
In this manuscript, we theoretically propose a multi-mode-fiber (MMF) based, artificial intelligence assisted spectrometer which is ultra-robust to temperature fluctuation. It has been demonstrated that the proposed spectrometer can reach a resolution of 0.1 pm and automatically reject the noise introduced by temperature fluctuation. The system is ultra-robust and with ultra-high spectral resolution which is beneficial for real life applications.
Integrated metasurface-freeform system enabled multi-focal planes augmented reality display
Shifei Zhang, Lina Gao, Yidan Zhao, Yongdong Wang, Bo Wang, Junjie Li, Jiaxi Duan, Dewen Cheng, Cheng-Wei Qiu, Yongtian Wang, Tong Yang, Lingling Huang
Opto-Electronic Science
2026-01-23
