Conventional optical high numerical aperture lenses are essential for high-resolution imaging, but bulky and expensive. In comparison, metalens-based optical components are the subjects of intensive investigation for their flexible manipulation of light. Methods of detecting and characterizing focal spots and scanning imaging produced by metalenses are well established. However, widefield imaging by metalenses is experimentally challenging. This study demonstrates the design and realization of silicon-based metalenses with numerical apertures of 0.447 and 0.204 in the broadband spectrum of 580–780 nm for microscopic widefield imaging. The optimized aspect ratio of the single nanorod is 5.1:1, which reduces the fabrication difficulty compared to other, more complicated designs and fabrication. Furthermore, we successfully demonstrate widefield imaging by the designed metalens and compare the simulated and the experimentally extracted modulation transfer function curves of the metalens.

A kind of optical data storage medium based on electron-trapping materials, Y3Al5O12:Ce3+ fluorescent ceramic, was developed by vacuum sintering technology. The medium shows sufficiently deep traps (1.67 and 0.77 eV). The properties of trap levels were researched by thermoluminescence curves, and the optical storage mechanism based on Ce3+ ion doping was proposed. More importantly, the data can be written-in by 254 nm UV light, and readout by heating (300°C). This work expands the application fields of fluorescent ceramics, and it is expected to promote the development of electron-trapping materials.

Electron-trapping materials have attracted a lot of attention in the field of optical data storage. However, the lack of suitable trap levels has hindered its development and application in the field of optical data storage. Herein, Lu₃Al₅O₁₂:Ce³⁺ fluorescent ceramics were developed as the optical storage medium, and high-temperature vacuum sintering induced the formation of deep traps (1.36 eV). The matrix based on the garnet-structured material ensures excellent rewritability. By analyzing the thermoluminescence and photostimulable luminescence, it is found that the transition of electrons provided by Ce³⁺ between the conduction band and trap levels offers the possibility for optical data storage. As evidence of its application, the optical information encoding using 254 nm light and decoding using a light stimulus and thermal stimulus were applied. These findings are expected to provide candidate material for novel optical storage technology, and further promote the development of advanced information storage technology.

In a dual-beam super-resolution laser direct-writing lithography system, the flatness error of the motion stage during XY-plane scanning (small fluctuations along the Z direction) is a critical factor that degrades writing accuracy. This work presents an integrated method combining dual-cylindrical-lens astigmatic measurement and machine-learning-based feedforward compensation. By employing the dual-cylindrical-lens astigmatic approach together with a four-quadrant detector (FQD) to capture variations of the spot profile, nanometer-scale Z-axis errors of the stage are converted into changes in the focus error signal (FES), enabling precise detection of flatness errors. To exploit the predictability of the error, this study further introduces random forest regression from ensemble learning. A prediction model is constructed through analysis of historical data to accurately estimate the spatial distribution of the stage flatness error, and the predicted error is then used to drive a piezoelectric actuator for feedforward compensation. Experimental results demonstrate that the proposed random-forest-based feedforward strategy markedly reduces flatness errors generated during planar motion, thereby improving both the writing accuracy and operational stability of the dual-beam super-resolution laser direct-writing lithography system.