Adaptive structured illumination optical-sectioning microscopy based on the prior knowledge of sample structure

Abstract

Fast and accurate online detection is of great significance to improve mass production efficiency and reduce the costs of semiconductor chips and other micro-nano devices. Compared with other 3D imaging detection techniques, structured illumination optical-sectioning microscopy has faster 3D imaging because it uses wide-field sinusoidal fringe structured illumination. However, commercial structured illumination optical-sectioning microscopes are designed for general needs, and the orientations of the fringe patterns they project are not optimized for the structures of the sample. To obtain an isotropic result, they need to project multiple sinusoidal fringe patterns with different orientations and perform multiple optical-sectioning imaging, and then average the optical-sectioning results, resulting in low imaging efficiency. This paper proposes an adaptive structured illumination optical-sectioning microscopy. It exploits prior knowledge of the sample structure to design the sinusoidal fringe orientation for each local structure and generates an adaptive structured illumination. In this way, we can achieve the best imaging effect with only one optical-sectioning imaging, thereby improving the imaging efficiency, which is beneficial to the improvement of the mass production efficiency of products. The method proposed in this paper will provide useful guidance on how to correctly and efficiently use structured illumination optical-sectioning microscopy to detect defects of micro-nano devices such as semiconductor chips with known designed structures.