A new publication from Opto-Electronic Science; DOI 10.29026/oes.2022.220012 considers high
frame-rate orbital-angular-momentum multiplexing holography.
In the information era, photons and electrons are the main carriers of information transmission.
Different from Moore's law in microelectronics, the density of integrated photonic devices is mainly
limited by the wavelength of light or diffraction limit. In order to improve the information transmission
capability of photonic devices, multiplexing technologies such as wavelength division multiplexing
(WDM), polarization division multiplexing (PDM) and mode division multiplexing (MDM) have been
widely and deeply studied, which effectively improve the transmission capacity of communication
systems. Recently, researchers combine these multiplexing technologies (such as WDM-MDM hybrid
multiplexing) to satisfy the enormous demand of ultra-high-link capacity in various scaled of optical
networks.
However, when extracting the wavelength-dependent information carried on independent modes, it is
usually necessary to cascade the MDM device and WDM device, which increases the footprint of the
device. Moreover, it is still a challenge to achieve both mode demultiplexing and spectral measurement
in a single device due to strong coupling effects between different guide modes. Therefore, the on-chip
multimode spectrometers compatible in MDM systems still remain as an open problem.
The authors of this article propose the concept of MDM spectroscopy, and describe the first integrated
mode-division demultiplexing spectrometer for MDM systems. This spectrometer consists of a
branched multimode waveguide and an array of photodetectors (Figure 1), achieving the integration of
mode demultiplexing and spectral detection by the dispersion of structure and photocurrent
measurement. Using deep learning techniques to solve inter-mode nonlinear coupling problem, the
researchers successfully realize the functions of single-shot reconstruction of multimode spectra and
multi-shot spectral resolution enhancement.
The simulation results show that the distribution of optical fields in the branched structure vary with
modes and wavelengths, inducing varied photocurrents on detectors. Empowered by deep learning
algorithms, the 15-nm spectral resolution of parallel reconstruction for TE1-TE4 is achieved by a singleshot
measurement of 25 detectors in the bandwidth of 1500-1600 nm. Moreover, the researchers
further applied the multimode reconstruction method to enhance spectral resolution, i.e., by stacking
the multimode response in TE1-TE4 to the single spectra at the time sequence, and the 3-nm resolution
is realized, which is improved by ~1.3 times compared with a single-mode response (7-nm resolution)
and breaks the resolving limitation by the number of detectors.
This work fuses mode identification and spectral recognition into a single device, breaking through the
conventional design strategy of MDM devices. It reveals more usages of guided modes, which sheds
light on new spectroscopic architectures for MDM systems.
Article reference 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
Keywords: computational spectroscopy / 2D-material photodetectors / mode-division demultiplexing /
deep learning / silicon photonics
