Zero crossing detection algorithm based on an MLP neural network for differential confocal microscopy

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.