Abstract
Differential confocal microscopy is widely used because of its ultra-high axial resolution. The surface gradient results in light loss, which decreases the slope of the differential response signal at zero crossing. At this point, when the signal-to-noise ratio is fixed, the traditional linear fitting method to determine the position of zero crossing is subject to significant error influence. To solve these issues, this paper proposes a zero crossing detection algorithm based on a multilayer perceptron (MLP) neural network. Experimental results reveal that the proposed algorithm is more robust and capable of better zero crossing extraction. When numerical aperture (NA)=0.4, the average error is 16.9 nm, which is 55.4 % higher than that of the traditional linear fitting algorithm. The proposed algorithm has a high potential for use with the differential confocal sensor to measure unknown steep surfaces.
Export citation and abstract BibTeX RIS
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.