Ultra-compact reconfigurable device for mode conversion and dual-mode DPSK demodulation via inverse design

Zheng, Ze-Huan; Chen, Ying; Chen, Huanyang; Chen, Jinhui

doi:10.1364/oe.420874

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…Target spectra are individually modulated into mode 1 -mode k in temporal slices T 1 -T k , generating k images of pixelated single-mode currents. The modulation operation can be implemented by reconfigurable mode converters 7,8 . After that, the samples composed of these k images are input into MRE-DNN to achieve resolution enhancement.…”

Section: Multi-shot Resolution-enhanced Spectral Reconstructionmentioning

confidence: 99%

“…In the past decades, mode-division multiplexing (MDM) technology has attracted considerable attention 2,3,4 , which divides mutually orthogonal mode channels in a single wavelength. Various kinds of mul-timode manipulating and processing devices have been springing up, such as mode multiplexers/demultiplexers 5,6 , mode converters [7][8][9] , and mode switches 10 . Due to the lack of devices that simultaneously integrate the functions of mode demultiplexing and spectral measurement, the sophisticated photo-detecting circuits are usually required when extracting the wavelength-dependent information carried on independent modes 11,12 .…”

Section: Introductionmentioning

confidence: 99%

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Towards integrated mode-division demultiplexing spectrometer by deep learning

Zheng¹,

Zhu²,

Chen³

et al. 2022

OES

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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.

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Section: Multi-shot Resolution-enhanced Spectral Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards integrated mode-division demultiplexing spectrometer by deep learning

Zheng¹,

Zhu²,

Chen³

et al. 2022

OES

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“…Deep learning technology exhibits the huge potential in photonic structure design, material optimization, and even the optimization of the entire optical system. Besides the aforementioned work, it has been used for various intricate devices design, such as multi-mode converters (Liu et al, 2019;Zheng et al, 2021), metagratings (Inampudi and Mosallaei, 2018;Jiang et al, 2019), chiral metamaterials (Ma et al, 2018) and photonic crystals (Hao et al, 2019).…”

Section: Inverse Design Of Metasurfacementioning

confidence: 99%

Deep Learning for Photonic Design and Analysis: Principles and Applications

Duan

Chen

et al. 2022

Front. Mater.

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Innovative techniques play important roles in photonic structure design and complex optical data analysis. As a branch of machine learning, deep learning can automatically reveal the inherent connections behind the data by using hierarchically structured layers, which has found broad applications in photonics. In this paper, we review the recent advances of deep learning for the photonic structure design and optical data analysis, which is based on the two major learning paradigms of supervised learning and unsupervised learning. In addition, the optical neural networks with high parallelism and low energy consuming are also highlighted as novel computing architectures. The challenges and perspectives of this flourishing research field are discussed.

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“…As the basic unit of MUX/DEMUX technique, mode converters with high conversion efficiency, low loss and wide bandwidth is indispensable. So far, most of the reported mode converters are realized by using specific structures such as directional coupler 2 , asymmetric Y-junctions 3,4 , and multimode interference couplers 5 . The design of above convertors rely on the experience of the designers and require a lot of time on structural parameter optimization.…”

Section: Introductionmentioning

confidence: 99%

Highly efficient on-chip mode conversion based on adjoint shape optimization

Yang

Tian

Liao

et al. 2023

AOPC 2022: Optoelectronics and Nanophotonics

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In this paper, highly efficient TE0-TE1 and TM0-TE1 conversions are achieved on SOI chips by optimizing the edge of mode converters employing the adjoint shape optimization method. At the central wavelength of 1550 nm, the conversion efficiencies of the device reaches 99.6% for TE0-TE1 conversion and 96.0% for TM0-TE1 conversion, while the loss are only 0.016 dB and 0.17 dB, respectively. Besides, the extinction ratio reaches 31.2 dB and 29.5 dB. The bandwidth characteristics of the devices are also numerically investigated. As the wavelength varies from 1500 nm to 1600 nm, the conversion efficiency for TE0-TE1 conversion can be kept above 96.6% and the extinction ratio is kept above 15.7 dB, while the insertion loss is kept below 0.14 dB. As for TM0-TE1 conversion, the conversion efficiency is above 92.6%, the extinction ratio is over 15.6 dB and insertion loss is below 0.33 dB within the wavelength range from 1505 nm to 1585 nm. Considering the influence of fabrication process on the performance of devices, the fabrication tolerance of mode converters is investigated by adjusting the width of devices. For both converters, the conversion efficiencies can be kept above 91.9%, while the insertion loss is less than 0.34 dB as the width variation of ± 20 nm at 1550 nm. The proposed mode converters take advantages of large bandwidth, high conversion efficiencies, low insertion loss and high fabrication tolerance, paving the path to realize efficient on-chip mode conversion in a cost-effective way.

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Ultra-compact reconfigurable device for mode conversion and dual-mode DPSK demodulation via inverse design

Cited by 9 publications

References 32 publications

Towards integrated mode-division demultiplexing spectrometer by deep learning

Towards integrated mode-division demultiplexing spectrometer by deep learning

Deep Learning for Photonic Design and Analysis: Principles and Applications

Highly efficient on-chip mode conversion based on adjoint shape optimization

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