Abstract
We explore a novel, to the best of our knowledge, approach to surface roughness metrology utilizing a single pixel, raster scanning single photon counting LiDAR system. It uses a collimated laser beam in picosecond pulses to probe a surface, capturing the changes of back-scattered photons from different points on the surface into a single mode fiber, and counting them using a single photon detector. These back-scattered photons carry speckle noise produced by the rough surface, and the variation in photon counts over different illumination points across the surface becomes a good measure of its roughness. By analyzing the variation frequency as the LiDAR scans over the surface using machine learning techniques, we demonstrate general measurements of surface roughness from 1.21 (1.27±4.51) to 102.01 (87.97±10.55) microns.
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