Recently, deep learning has yielded transformative success across optics and photonics, especially in optical metrology. Deep neural networks (DNNs) with a fully convolutional architecture (e.g., U-Net and its derivatives) have been widely implemented in an end-to-end manner to accomplish various optical metrology tasks, such as fringe denoising, phase unwrapping, and fringe analysis. However, the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best na?ve, as the physical laws governing the image formation or other domain expertise pertaining to the measurement have not yet been fully exploited in current deep learning practice. To this end, we introduce a physics-informed deep learning method for fringe pattern analysis (PI-FPA) to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry (LeFTP) module. By parameterizing conventional phase retrieval methods, the LeFTP module embeds the prior knowledge in the network structure and the loss function to directly provide reliable phase results for new types of samples, while circumventing the requirement of collecting a large amount of high-quality data in supervised learning methods. Guided by the initial phase from LeFTP, the phase recovery ability of the lightweight DNN is enhanced to further improve the phase accuracy at a low computational cost compared with existing end-to-end networks. Experimental results demonstrate that PI-FPA enables more accurate and computationally efficient single-shot phase retrieval, exhibiting its excellent generalization to various unseen objects during training. The proposed PI-FPA presents that challenging issues in optical metrology can be potentially overcome through the synergy of physics-priors-based traditional tools and data-driven learning approaches, opening new avenues to achieve fast and accurate single-shot 3D imaging.

研究了一种硅光子芯片空间光耦合系统，简化了集成光子芯片用光纤阵列耦合的方式，通过将1550 nm附近波段的光通过空间光学系统，经光栅耦合器垂直耦合进入集成光子芯片，经过波导与微环谐振腔实现光学传感，采用多模光纤收集出射能量，并进行温度传感测试。实验结果表明，用波长探测方式，得到自由光谱范围（FSR）为0.85 nm，Q值为16321的环形谐振腔直通端透射谱线。在温度传感测试中，其灵敏度达到127 pm/℃。通过进一步对比测试表明，空间光耦合与光纤阵列光耦合只在插入损耗方面有所差距，耦合系统可得到较好的温度传感测试结果。空间光耦合具有更换传感器芯片便捷，容易实现多通道测试等优点。

为了测试高速通信光模块在极端环境下的工作性能，并提升其出厂测试效率，设计了高速通信光模块热控系统。使用该系统不仅可以实现单独测试QSFP-DD封装模式的光模块，还可以实现双通道并行测试QSFP-28封装模式的光模块，成功使光模块测试效率提升一倍。首先，根据半导体制冷器的特性设计了待测试件热电制冷器组件，Flotherm的仿真结果表明热电制冷器组件可用。接着，根据半导体制冷器的原理及特性，设计了热排散系统。最后，将热控系统与水冷机的控温效率和效果做对比。实验结果表明：热控系统可以在110 s内实现光模块壳温在0~65 ℃之间的快速调控。热控系统基本满足对常用封装方式的高速通信光模块的控温需求，且相对于水冷机而言，具有小型化、低噪音、零震动的优势，更利于光模块集成化测试。

Based on the Vernier effect of the cascaded double ring resonator (CDRR) sensor, a sensor consisting of a microfluidic control system, a sensing ring, and a reference ring with a micro-heater for thermal tuning is proposed in this paper. In wavelength interrogation, a broadband spectrometer or a large tunable range laser is not required. The shift of the output spectrum caused by the refractive index change of the sample is converted into the change of the electric power by the thermal tuning heater. Due to the Vernier effect, the sensitivity of the sensor is 20 times higher than that of the single ring. The spectral envelope of the thermal-optic tuning of the sensor was fitted by a Gaussian function in the wavelength range of 8 nm. The experimental results showed that the sensitivity was 33.703 W/RIU, and the limit of detection was 1.34×10-5RIU.

为研究不同金属材料的激光烧蚀推进性能，对常见的七种金属材料：Al,Fe,Ni,Cu,Y,Ag,Au，使用波长1 064 nm，脉宽8 ns的Nd:YAG激光器在大气下进行烧蚀，测量了烧蚀质量、冲量、冲量耦合系数、比冲和能量转化效率等推进性能参数，获得了激光功率密度对推进性能的影响规律。实验结果表明：相同激光功率密度下，Fe的烧蚀质量最大，Y的烧蚀质量最小；Al,Au,Cu的冲量较大，Ag的冲量最小；Au的冲量耦合系数和比冲均值在七种金属材料中最大，分别在激光功率密度为 $1.72 \times {10^{10}}\;{\rm{W}}/{\rm{c}}{{\rm{m}}^2}$和 ${{2}}{{.98}} \times {10^{10}}\;{\rm{W}}/{\rm{c}}{{\rm{m}}^2}$时达到40.7 μN/W和500 s的最大值，平均能量转化效率可达6%。