Mode-division multiplexing (MDM) technology enables high-bandwidth data transmission using orthogonal waveguide modes to construct parallel data streams. However, few demonstrations have been realized for generating and supporting high-order modes, mainly due to the intrinsic large material group-velocity dispersion (GVD), which make it challenging to selectively couple different-order spatial modes. We show the feasibility of on-chip GVD engineering by introducing a gradient-index metamaterial structure, which enables a robust and fully scalable MDM process. We demonstrate a record-high-order MDM device that supports TE₀–TE₁₅ modes simultaneously. 40-GBaud 16-ary quadrature amplitude modulation signals encoded on 16 mode channels contribute to a 2.162 Tbit / s net data rate, which is the highest data rate ever reported for an on-chip single-wavelength transmission. Our method can effectively expand the number of channels provided by MDM technology and promote the emerging research fields with great demand for parallelism, such as high-capacity optical interconnects, high-dimensional quantum communications, and large-scale neural networks.

针对目前双面微器件加工方法步骤繁琐、效率低的问题，提出基于改进Gerchberg-Saxton（GS）算法的全息双面光刻方法，使用单个光源在玻璃基底的上下表面同时曝光，进行双面图形的制作。该方法通过计算不同轴向位置图案对应的组合全息图，并将其加载到空间光调制器（LCOS-SLM）上，对入射光场进行调制，从而在目标空间内实现双面图形重现。采用改进GS算法对距离焦面2 mm处的图案A与距离焦面4.06 mm处的图案B进行全息图计算与仿真重建。搭建实验装置，对3 mm厚透明石英玻璃基底的上下表面同时曝光，且对光场生成过程中的散斑、杂散光及串扰问题做出分析并提出解决方案，最终实现60 μm线宽双层图案曝光，验证了所提方法进行双面光刻的可行性。所提方法使用单张全息图和单个光源，通过单次曝光即可在目标体积内生成多层任意图形，极大地简化了双面图形制作的步骤。

Structured illumination microscopy (SIM) has been widely applied in the superresolution imaging of subcellular dynamics in live cells. Higher spatial resolution is expected for the observation of finer structures. However, further increasing spatial resolution in SIM under the condition of strong background and noise levels remains challenging. Here, we report a method to achieve deep resolution enhancement of SIM by combining an untrained neural network with an alternating direction method of multipliers (ADMM) framework, i.e., ADMM-DRE-SIM. By exploiting the implicit image priors in the neural network and the Hessian prior in the ADMM framework associated with the optical transfer model of SIM, ADMM-DRE-SIM can further realize the spatial frequency extension without the requirement of training datasets. Moreover, an image degradation model containing the convolution with equivalent point spread function of SIM and additional background map is utilized to suppress the strong background while keeping the structure fidelity. Experimental results by imaging tubulins and actins show that ADMM-DRE-SIM can obtain the resolution enhancement by a factor of ∼1.6 compared to conventional SIM, evidencing the promising applications of ADMM-DRE-SIM in superresolution biomedical imaging.

Various super-resolution microscopy techniques have been presented to explore fine structures of biological specimens. However, the super-resolution capability is often achieved at the expense of reducing imaging speed by either point scanning or multiframe computation. The contradiction between spatial resolution and imaging speed seriously hampers the observation of high-speed dynamics of fine structures. To overcome this contradiction, here we propose and demonstrate a temporal compressive super-resolution microscopy (TCSRM) technique. This technique is to merge an enhanced temporal compressive microscopy and a deep-learning-based super-resolution image reconstruction, where the enhanced temporal compressive microscopy is utilized to improve the imaging speed, and the deep-learning-based super-resolution image reconstruction is used to realize the resolution enhancement. The high-speed super-resolution imaging ability of TCSRM with a frame rate of 1200 frames per second (fps) and spatial resolution of 100 nm is experimentally demonstrated by capturing the flowing fluorescent beads in microfluidic chip. Given the outstanding imaging performance with high-speed super-resolution, TCSRM provides a desired tool for the studies of high-speed dynamical behaviors in fine structures, especially in the biomedical field.

斜面和曲面微结构元件在微电子学、微光学、微流体学等领域有着重要的应用，为了实现快速、低成本的斜面和曲面光刻，提出了利用基于液晶空间光调制器的纯相位计算全息技术投影目标图案到斜面和曲面进行曝光的方法。生成了斜面和球面全息光场，对光场进行消散斑和杂散光去除的处理，完成了斜面和球面光刻实验验证。实验结果表明：该方法加工效率高、设计灵活多变，不受单一结构限制，是一种极具潜力的三维微纳加工方法。

现有的空间失配标定方法受原理限制只能实现像素级的标定精度，且易受环境噪声干扰。本文提出了一种亚像素级的同步微离轴数字全息显微系统的空间失配标定方法。该方法在建模时不仅分析了图像分割导致的横向位置误差，还进一步考虑了传感器倾斜引入的纵向位置误差，并将标定过程归纳为求解非线性多变量优化问题。在本文中，粒子群优化算法因其结构简单、收敛效率高、全局搜索能力强等优势，被用于解决这一优化问题。在标定过程中，建立了基于相位畸变的纯相位波面，并将纯相位波前的均方根误差作为目标函数，以去除噪声对标定精度的影响。仿真结果证明本文方法具有亚像素级精度，实验证明了其在实际系统中的有效性。