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Towards integrated mode-division demultiplexing spectrometer by deep learning
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作者 Ze-huan Zheng sheng-ke zhu +2 位作者 Ying Chen Huanyang Chen Jin-hui Chen 《Opto-Electronic Science》 2022年第11期21-35,F0004,共16页
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-divis... 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 TE_(1)-TE_(4) by Tikhonov regularization optimization.Empowered by deep learning algorithms,the 15-nm resolution of parallel reconstruction for TE_(1)-TE_(4) is achieved by a single-shot measurement.Moreover,by stacking the multimode response in TE_(1)-TE_(4) 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. 展开更多
关键词 computational spectroscopy 2D-material photodetectors mode-division demultiplexing deep learning silicon photonics
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