26 June 2017 Uncertainty of scattered light roughness measurements based on speckle correlation methods
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Surface micro topography measurement (e.g., form, waviness, roughness) is a precondition to assess the surface quality of technical components with regard to their applications. Well defined, standardized measuring devices measure and specify geometrical surface textures only under laboratory conditions. Laser speckle-based roughness measurement is a parametric optical scattered light measuring technique that overcomes this confinement. Field of view dimensions of some square millimeters and measuring frequencies in the kHz domain enable in-process roughness characterization of even moving part surfaces. However, camera exposure times of microseconds or less and a high detector pixel density mean less light energy per pixel due to the limited laser power. This affects the achievable measurement uncertainty according to the Heisenberg uncertainty principle. The influence of fundamental, inevitable noise sources such as the laser shot noise and the detector noise is not quantified yet. Therefore, the uncertainty for speckle roughness measurements is analytically estimated. The result confirms the expected inverse proportionality of the measurement uncertainty to the square root of the illuminating light power and the direct proportionality to the detector readout noise, quantization noise and dark current noise, respectively. For the first time it is possible to quantify the achievable measurement uncertainty u(Sa) < 1 nm for the scattered light measuring system. The low uncertainty offers ideal preconditions for in-process roughness measurements in an industrial environment with an aspired resolution of 1 nm.
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Stefan Patzelt, Stefan Patzelt, Dirk Stöbener, Dirk Stöbener, Gerald Ströbel, Gerald Ströbel, Andreas Fischer, Andreas Fischer, } "Uncertainty of scattered light roughness measurements based on speckle correlation methods", Proc. SPIE 10329, Optical Measurement Systems for Industrial Inspection X, 103291P (26 June 2017); doi: 10.1117/12.2269171; https://doi.org/10.1117/12.2269171

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