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(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
Optoelectronics Letters, 2021-12-10
https://doi.org/10.1007/s11801-021-1126-y
https://link.springer.com/article/10.1007/s11801-021-1126-y
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.
Deep learning enables temperature-robust spectrometer with high resolution_1
Deep learning enables temperature-robust spectrometer with high resolution_2
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