(Peer-Reviewed) Towards integrated mode-division demultiplexing spectrometer by deep learning
Ze-huan Zheng 郑泽寰 ¹ ², Sheng-ke Zhu 朱圣科 ¹ ⁴, Ying Chen 陈颖 ³, Huanyang Chen 陈焕阳 ⁵, Jin-hui Chen 陈锦辉 ¹ ⁴ ⁶
¹ Shenzhen Research Institute, Xiamen University, Shenzhen 518000, China
中国 深圳 厦门大学深圳研究院
² Xiamen Power Supply Bureau of Fujian Electric Power Company Limited, State Grid, Xiamen 361004, China
中国 厦门 福建省电力有限公司 厦门供电局
³ College of Information Science and Engineering, Fujian Provincial Key Laboratory of Light Propagation and Transformation, Huaqiao University, Xiamen 361021, China
福建省光传输与变换重点实验室 华侨大学信息科学与工程学院
⁴ Institute of Electromagnetics and Acoustics, Xiamen University, Xiamen 361005, China
中国 厦门 厦门大学电磁声学研究院
⁵ College of Physical Science and Technology, Xiamen University, Xiamen 361005, China
中国 厦门 厦门大学物理科学与技术学院
⁶ Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China
中国 厦门 中国福建能源材料科学与技术创新实验室(嘉庚创新实验室)
Opto-Electronic Science, 2022-11-01

Miniaturized spectrometers have been widely researched in recent years, but few studies are conducted with on-chip multimode schemes for mode-division multiplexing (MDM) systems. Here we propose an ultracompact mode-division demultiplexing spectrometer that includes branched waveguide structures and graphene-based photodetectors, which realizes simultaneously spectral dispersing and light fields detecting.

In the bandwidth of 1500–1600 nm, the designed spectrometer achieves the single-mode spectral resolution of 7 nm for each mode of TE1–TE4 by Tikhonov regularization optimization. Empowered by deep learning algorithms, the 15-nm resolution of parallel reconstruction for TE1–TE4 is achieved by a single-shot measurement. Moreover, by stacking the multimode response in TE1–TE4 to the single spectra, the 3-nm spectral resolution is realized.

This design reveals an effective solution for on-chip MDM spectroscopy, and may find applications in multimode sensing, interconnecting and processing.
Towards integrated mode-division demultiplexing spectrometer by deep learning_1
Towards integrated mode-division demultiplexing spectrometer by deep learning_2
Towards integrated mode-division demultiplexing spectrometer by deep learning_3
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