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3D printing of optics has gained significant attention in optical industry, but most of the research has been focused on organic polymers. In spite of recent progress in 3D printing glass, 3D printing of precision glass optics for imaging applications still faces challenges from shrinkage during printing and thermal processing, and from inadequate surface shape and quality to meet the requirements for imaging applications. This paper reports a new liquid silica resin (LSR) with higher curing speed, better mechanical properties, lower sintering temperature, and reduced shrinkage, as well as the printing process for high‐precision glass optics for imaging applications. It is demonstrated that the proposed material and printing process can print almost all types of optical surfaces, including flat, spherical, aspherical, freeform, and discontinuous surfaces, with accurate surface shape and high surface quality for imaging applications. It is also demonstrated that the proposed method can print complex optical systems with multiple optical elements, completely removing the time‐consuming and error‐prone alignment process. Most importantly, the proposed printing method is able to print optical systems with active moving elements, significantly improving system flexibility and functionality. The printing method will enable the much‐needed transformational manufacturing of complex freeform glass optics that are currently inaccessible with conventional processes.

 

We propose an on-axis deflectometric system for the accurate measurement of freeform surfaces with large slope ranges. A miniature plane mirror is attached on the illumination screen to fold the optical path and achieve the on-axis deflectometric testing. Due to the existence of the miniature folding mirror, the deep-learning method is applied to recover the missing surface data in a single measurement. Low sensitivity to the calibration error of system geometry and high testing accuracy can be achieved with the proposed system. The feasibility and accuracy of the proposed system have been validated. The system is low in cost and simple in configuration, and it provides a feasible way for the flexible and general testing of freeform surfaces, with a significant potential of the application in on-machine testing.

 

On the demand of low-cost, lightweight, miniaturized, and integrated optical systems, precision lenslet arrays are widely used. Diamond turning is often used to fabricate lenslet arrays directly or molds that are used to mold lenslet arrays. In this paper, mainly by real-time monitoring position following error for slow tool servo, different fabrication parameters are quantitatively studied and optimized for actual fabrication, then by actual fabrication validation, uniform and high-fidelity surface topography across the actual whole lenslet array is achieved. The evaluated fabrication parameters include sampling strategy, inverse time feed, arc-length, etc. The study provides a quick, effective, and detailed reference for both convex and concave lenslet array cutting parameter selection. At the end, a smooth zonal machining strategy toolpath is demonstrated for fabricating concave lenslet arrays.

 

Polarization aberrations are found in most optical components due to a materials-differing response tos- andp-polarizations. This differing response can manifest either as diattenuation, retardance, or both. Correction of polarization aberrations, such as these, are critical in many applications such as interferometry, polarimetry, display, and high contrast imaging, including astronomy. In this work, compensators based on liquid crystal polymer and anti-reflection thin-films are presented to correct polarization aberrations in both transmission and reflection configurations. Our method is versatile, allowing for good correction in transmission and reflection due to optical components possessing differing diattenuation and retardance dispersions. Through simulation and experimental validation we show two designs, one correcting the polarization aberrations of a dichroic spectral filter over a 170nm wavelength band, and the other correcting the polarization aberration of an aluminum-coated mirror over a 400nm wavelength band and a 55-degree cone of angles. The measured performance of the polarization aberration compensators shows good agreement with theory.

 

Elliptical retarders have important applications in interferometry and polarimetry, as well as imaging and display technologies. In this work, we discuss the traditional elliptical retarder decomposition using Pauli matrices as basis sets and then introduce a solution to the inverse problem: how an arbitrary elliptical retarder with desired eigenpolarizations and retardance can be constructed using a combination of linear and circular retarders. We present a simple design process, based on eigen-decomposition, with a solution determined by the intrinsic properties of each individual retarder layer. Additionally, a novel use of cholesteric liquid crystal polymer as a circular retarder is presented. Through simulation and experimental validation, we show cholesteric phase liquid crystal has an achromatic region of circular retardance at shorter wavelengths, outside of the Bragg regime. Finally, we verify our design process by fabricating and testing an elliptical retarder using both nematic and cholesteric phase liquid crystal polymers. The performance of the elliptical retarders shows excellent agreement with theory.

 

Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.