Multimode fiber (MMF) spectrometers suffer from the resolution-bandwidth trade-off due to the limited spatial speckle information used for spectral recovery. We demonstrate a design of an MMF spectrometer with scalable bandwidth using space-division multiplexing. A multicore fiber (MCF) is used to integrate with the MMF. The spatial degrees of freedom at the input are exploited to provide the independent speckle pattern, thus multiplying the spatial information and scaling the bandwidth. We have experimentally achieved 30 nm bandwidth with 0.02nm resolution at wavelength 1550 nm, only using 3 cores of a 7-core fiber and a single MMF. An efficient algorithm is developed to reconstruct the broadband sparse and dense spectrums accurately. The approach can enhance the operating bandwidth of MMF spectrometers without sacrificing the resolution, and simultaneously ensure the system complexity and stability.

Chiral plasmonic metasurfaces are promising for enlarging the chiral signals of biomolecules and improving the sensitivity of bio-sensing. However, the design process of the chiral plasmonic nanostructures is time consuming. Deep learning has been playing a key role in the design of photonic devices with high time efficiency and good design performance. This paper proposes a deep neural network (DNN) to achieve forward prediction and inverse design for 3D chiral plasmonic metasurfaces, and further improve the training speed and performance by the transfer learning method. Once the DNNs are trained using a part of the sampled data from the parameter space, the circular dichroism (CD) spectra can be predicted within the time on milliseconds (about 3.9 ms for forward network and 5.6 ms for inverse network) with high prediction accuracy. The inverse design was optimized by taking more spectral information into account and extracting the critical features using the one-dimensional convolutional kernel. The aforementioned trained network for one handedness can accelerate the training speed and improve performance with small datasets for the opposite handedness via the transfer learning method. The proposed approach is instructive in the design process of chiral plasmonic metasurfaces and could find applications in exploring versatile complex nanophotonic devices efficiently.

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Due to the small core diameter, a single-core multimode fiber (MMF) has been extensively investigated for endoscopic imaging. However, an extra light path is always utilized for illumination in MMF imaging system, which takes more space and is inapplicable in practical endoscopy imaging. In order to make the imaging system more practical and compact, we proposed a dual-function MMF imaging system, which can simultaneously transmit the illumination light and the images through the same imaging fiber. Meanwhile, a new deep learning-based encoder-decoder network with full-connected (FC) layers was designed for image reconstruction. We conducted an experiment of transmitting images via a 1.6 m long MMF to verify the effectiveness of the dual-function MMF imaging system. The experimental results show that the proposed network achieves the best reconstruction performance compared with the other four networks on different datasets. Besides, it is worth mentioning that the cropped speckle patterns can still be used to reconstruct the original images, which helps to reduce the computing complexity significantly. We also demonstrated the ability of cross-domain generalization of the proposed network. The proposed system shows the potential for more compact endoscopic imaging without external illumination.

The severe band-limited effect resulted from the low-cost optical transceiver increases the channel memory length and the number of taps of the equalizers. Besides, the interaction of fiber dispersion and square-law detection introduce nonlinear distortions in intensity modulation and direct-detection (IM/DD) transmission systems. The serious band-limited effect and nonlinear distortions degrade the transmission performance and bring challenges to current equalizers for low-complexity implementation. In this paper, we propose a trellis-compression nonlinear maximum likelihood sequence estimation (TC-NL-MLSE) algorithm to compensate the linear and nonlinear distortions with lower complexity. In the TC-NL-MLSE, we introduce a polynomial nonlinear filter (PNLF) to partly compensate both the linear distortions and nonlinear distortions. Then, we establish a look-up-table (LUT) to calculate the nonlinear branch metric (BM). To simplify the calculation, two or three levels with the highest probabilities are selected according to decision thresholds for each symbol to compress the state-trellis graph (STG). This significantly reduces computational complexity on BM calculations especially for high-order modulations. We conduct experiments to transmit beyond the 200-Gb/s PAM-8 signal over 2-km standard single mode fiber (SSMF) at C-band. The TC-NL-MLSE outperforms the reduced-state MLSE with PNLF, and can reach the 7%-overhead hard-decision forward error correction threshold. Moreover, the TC-NL-MLSE reduces the complexity by 97% compared with standard LUT-MLSE, limiting the multipliers around 100 at the expense of only 0.2-dB receiver sensitivity penalty.