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.

In recent years, there has been tremendous progress in the development of deep-learning-based approaches for optical metrology, which introduce various deep neural networks (DNNs) for many optical metrology tasks, such as fringe analysis, phase unwrapping, and digital image correlation. However, since different DNN models have their own strengths and limitations, it is difficult for a single DNN to make reliable predictions under all possible scenarios. In this work, we introduce ensemble learning into optical metrology, which combines the predictions of multiple DNNs to significantly enhance the accuracy and reduce the generalization error for the task of fringe-pattern analysis. First, several state-of-the-art base models of different architectures are selected. A K-fold average ensemble strategy is developed to train each base model multiple times with different data and calculate the mean prediction within each base model. Next, an adaptive ensemble strategy is presented to further combine the base models by building an extra DNN to fuse the features extracted from these mean predictions in an adaptive and fully automatic way. Experimental results demonstrate that ensemble learning could attain superior performance over state-of-the-art solutions, including both classic and conventional single-DNN-based methods. Our work suggests that by resorting to collective wisdom, ensemble learning offers a simple and effective solution for overcoming generalization challenges and boosts the performance of data-driven optical metrology methods.

Significant optical engineering advances at the University of Arizona are being made for design, fabrication, and construction of next generation astronomical telescopes. This summary review paper focuses on the technological advances in three key areas. First is the optical fabrication technique used for constructing next-generation telescope mirrors. Advances in ground-based telescope control and instrumentation comprise the second area of development. This includes active alignment of the laser truss-based Large Binocular Telescope (LBT) prime focus camera, the new MOBIUS modular cross-dispersion spectroscopy unit used at the prime focal plane of the LBT, and topological pupil segment optimization. Lastly, future space telescope concepts and enabling technologies are discussed. Among these, the Nautilus space observatory requires challenging alignment of segmented multi-order diffractive elements. The OASIS terahertz space telescope presents unique challenges for characterizing the inflatable primary mirror, and the Hyperion space telescope pushes the limits of high spectral resolution, far-UV spectroscopy. The Coronagraphic Debris and Exoplanet Exploring Pioneer (CDEEP) is a Small Satellite (SmallSat) mission concept for high-contrast imaging of circumstellar disks and exoplanets using vector vortex coronagraph. These advances in optical engineering technologies will help mankind to probe, explore, and understand the scientific beauty of our universe.

在已有的紫外、可见和近红外波段的光谱辐射亮度国家基准的基础上，将光谱辐射亮度的测量范围向红外波段扩展，建立2 μm～14 μm红外光谱辐射亮度计量基准装置，可为遥感对地观测、气候变化、目标识别、材料发射率测量等领域的红外光谱辐射定标提供技术支撑。针对红外光谱辐射亮度测量中的温度均匀性和源尺寸效应进行研究，通过定制光阑或限制所用腔口位置实现了温度均匀性的提升；采用光学仿真、增加光阑和简化光路等方法进行了系统源尺寸效应的分析和抑制，有效地降低了源尺寸效应的不确定度。下一步将对系统的非线性效应等参数进行研究，并对整套系统的不确定度进行评估。

随着同步辐射光源和自由电子激光器相关技术的发展和光束质量的提升，对用于转递和聚焦光束能量的X光反射镜的指标要求也逐渐提高。为避免引入额外的波前误差，反射镜面形高度误差均方根值的要求已逼近至亚纳米量级。如此苛刻的面形要求对X光反射镜的测量工作带来了极大的困难和挑战。除了在各国同步辐射光源得到广泛使用的长程轮廓仪等基于角度测量的轮廓扫描仪器之外，基于激光干涉仪的拼接干涉技术也发展为测量同步辐射镜的一种有效手段。文中主要介绍了近期笔者等为测量X光反射镜而开发的拼接干涉平台。利用这一测量平台，研究了在不同的拼接参数下的多种拼接模式。着重讲述了其中纯软件拼接模式的基本原理和实际测量。用实测结果与不同测量仪器和不同研究机构的结果进行比对，验证了拼接干涉测量用于检测同步辐射镜的有效性，并展示了此拼接平台的测量表现。根据所得的测量数据看来，使用纯软件拼接模式来测量X光平面反射镜时，测量重复性的均方差值可以达到0.1 nm左右；而测量X光双曲柱面镜时（曲率半径的变化范围为50~130 m），测量重复性的均方差值为0.2~0.3 nm。此结果基本满足平面和接近平面（曲率半径大于50 m）的同步辐射镜常规检测和为确定性加工提供面形反馈的需要。