Surface roughness metrology with a raster scanning single photon LiDAR

Daniel Tafone, Luke McEvoy, Yong Meng Sua, Yu Ping Huang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)7917-7922
Number of pages6
JournalApplied Optics
Volume63
Issue number30
DOIs
StatePublished - 20 Oct 2024

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