Accelerated Nano-Optical Imaging through Sparse Sampling

doi:10.1021/acs.nanolett.3c03733

Accelerated Nano-Optical Imaging through Sparse Sampling

2024-02-08

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DOI：10.1021/acs.nanolett.3c03733

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The integration time and signal-to-noise ratio are inextricably linked when performing scanning probe microscopy based on raster scanning. This often yields a large lower bound on the measurement time, for example, in nano-optical imaging experiments performed using a scanning near-field optical microscope (SNOM). Here, we utilize sparse scanning augmented with Gaussian process regression to bypass the time constraint. We apply this approach to image charge-transfer polaritons in graphene residing on ruthenium trichloride (alpha-RuCl3) and obtain key features such as polariton damping and dispersion. Critically, nano-optical SNOM imaging data obtained via sparse sampling are in good agreement with those extracted from traditional raster scans but require 11 times fewer sampled points. As a result, Gaussian process-aided sparse spiral scans offer a major decrease in scanning time.

Keywords:

scanning near-fieldoptical microscopy (snom)

gaussianprocess regression

sparse sampling

polaritons

Accelerated Nano-Optical Imaging through Sparse Sampling

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