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Zheng ZH, Zhu SK, Chen Y, Chen HY, Chen JH. Towards integrated mode-division demultiplexing spectrometer by deep learning. Opto-Electron Sci 1, 220012 (2022). doi: 10.29026/oes.2022.220012
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Original Article Open Access
Towards integrated mode-division demultiplexing spectrometer by deep learning
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Abstract
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. -
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References
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Zheng ZH, Zhu SK, Chen Y, Chen HY, Chen JH. Towards integrated mode-division demultiplexing spectrometer by deep learning. Opto-Electron Sci 1, 220012 (2022). doi: 10.29026/oes.2022.220012
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Figure 1.
(a) Illustration of the proposed mode-division demultiplexing (MDD) spectrometer. The multimode spectral signal (mode1–modek) dispersed in the branched waveguide structures and detected by graphene-photodetector units. The generated photocurrents I1–Im at unit1–unitm from the input multimode spectral signals are fed into a deep neural network (DNN) to extract the desired spectral profiles F1 (λ)–Fk (λ). (b) Structural parameters of a designed branched waveguide and graphene photodetector. Inset: the cross-section view of a silicon waveguide and graphene photodetector.
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Figure 2.
Simulated characterizations of the proposed device for the single-mode (TE1–TE4) spectrometer by Tikhonov regularization optimization. (a) Normalized photoresponses of two typical spectral detecting units. (b) Correlation functions of the spectral responses for different TE modes. (c, d) Recovered spectral profiles of dual peaks separated by 7 nm (c) and the same random spectra (d) for TE1–TE4.
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Figure 3.
Principle of the single-shot multimode reconstruction (SMR) algorithm. The unknown multimode spectra are imported to the proposed MDD spectrometer at the same time. The response photocurrents I1-Im are captured at unit1-unitm, and then are fed into the trained SMR-DNN as a pixel image to implement simultaneous reconstruction of different mode spectra in single shot.
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Figure 4.
Single-shot simultaneous multimode reconstructions from the trained SMR-DNN for TE1–TE4. (a) Dual spectral peaks resolution with light wavelength separated by 15 nm, (b) single peaks with FWHM-15 nm uniformly distributed in the bandwidth of 1500-1600 nm. (c–f) The construction of multimode spectra in different modes: (c) TE1, (d) TE2, (e) TE3, and (f) TE4.
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Figure 5.
Principle and performance of the designed multi-shot resolution-enhanced (MRE) architecture. (a) Principle of the MRE algorithm. The various single-mode photocurrent (T1–Tk) images are generated by the spectral responses of photodetecting arrays and are fed into MRE-DNN to obtain resolution-improved results. (b–d) Recovered results of dual spectral peaks with light wavelength separated by 3 nm (b), single peaks with FWHM-3 nm uniformly distributed in the bandwidth (c), and the constructed random spectra (d).
