Hot Paper - Deep learning enables temperature-robust spectrometer with high resolution

Deep learning enables temperature-robust spectrometer with high resolution

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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

https://doi.org/10.1007/s11801-021-1126-y

https://link.springer.com/article/10.1007/s11801-021-1126-y

Optoelectronics Letters, 10 December 2021

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.

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