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Super-Resolution Imaging of Semiconductor Nanopatterns Using the Non-Bridging-Oxygen-Hole-Center-Based Photoluminescence Enhancement Effect

光致发光 纳米技术 纳米尺度 半导体 纳米材料 分辨率(逻辑) 计量学 图像分辨率 材料科学 纳米结构 光电子学 化学 计算机科学 光学 物理 人工智能
作者
L. Hung,Uidon Jeong,Ga‐eun Go,Doory Kim
出处
期刊:Journal of the American Chemical Society [American Chemical Society]
标识
DOI:10.1021/jacs.5c09567
摘要

Precise metrology and defect characterization are essential for ensuring device performance and yield as semiconductor manufacturing pushes critical dimensions into the nanoscale regime. In this regard, super-resolution fluorescence microscopy (SRM) has recently gained attention as a powerful tool, providing nanoscale resolution essential for advanced device fabrication. However, conventional SRM techniques often rely on fluorescent labeling, which could introduce chemical complexity, alter material properties, and limit semiconductor-processing compatibility while also imposing resolution constraints owing to the size of the fluorescent tag. To address these challenges, we introduce a new super-resolution label-free imaging approach for silica-based nanomaterials based on the photoluminescence generated from the formation of nonbridging oxygen hole centers (NBOHCs) in silica. These intrinsic defects exhibit strong photoluminescence that is significantly enhanced through electron illumination and thiol treatment, thereby enabling subdiffraction imaging without the need for complex molecular labels. Furthermore, we reconstructed super-resolution images of silica nanostructures with significantly improved spatial resolution through the use of fluctuation-based super-resolution image analysis. We show that this method effectively resolves silica-based semiconductor nanopatterns in a manner that delivers high contrast and spatial resolution by highlighting the NBOHC-regeneration potential for advancing semiconductor metrology. Also, we demonstrate that our method enables high-contrast and highly sensitive visualization of various nanoscale defects on patterned silica surfaces, highlighting its strong potential as a powerful, nondestructive tool for nanoscale defect detection in semiconductor pattern inspection. Ultimately, the developed method is expected to provide a promising approach that can be broadly used to structurally characterize various inorganic nanomaterials at the nanoscale.
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