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25 October 2018 A scanning near-field optical microscope applied to commercial environmental scanning electron microscopes
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Abstract
A Scanning Near-field Optical Microscope (SNOM) devoted to a commercial Environmental Scanning Electron Microscope (ESEM) is demonstrated in this paper. The system can be applied to biological samples and optoelectronic devices at the nanoscale to obtain their surface topography information and fluorescence spectroscopic information in situ. Optical microscopes can characterize function and material components by means of fluorescence spectroscopic technology. However, the spatial resolution of conventional optical microscopes is limited by the diffraction limit. Owing to fine surface topography information of the sample, a SNOM combined with environmental scanning electron microscopy can obtain fluorescence images and fine surface topography images simultaneously. An Atomic Force Microscope (AFM), which utilizes the same optical fiber probe with the SNOM, is added to the system. The fluorescence signal acquired by the SNOM and the surface topography signal acquired by the AFM have the same coordinate. The surface topography images of the AFM are matched to the surface topography images of the ESEM. Therefore, the fluorescence images are located to the ESEM images. A quartz tuning fork offers feedback signals to control the distance between the optical fiber probe and the samples. The samples are scanned by a high-precision scanner with 0.3 nm resolution in X and Y directions. Coase approach to samples and selection of scanning area are achieved by a micromanipulator. Quantum dots samples and polystyrene (PS) spheres samples are prepared, and their surface topography images and fluorescence images are obtained. The spatial resolution of the SNOM applied to the commercial ESEM is less than 100 nm.
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Ping Gong, Liang Xie, and Huixia Yang "A scanning near-field optical microscope applied to commercial environmental scanning electron microscopes", Proc. SPIE 10822, Real-time Photonic Measurements, Data Management, and Processing III, 108220D (25 October 2018); https://doi.org/10.1117/12.2500794
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