Hybrid-net: a two-to-one deep learning framework for three-wavelength phase-shifting interferometry

Abstract

In this paper, we propose a two-to-one deep learning (DL) framework for three- wavelength phase-shifting interferometry. The interferograms at two different wavelengths are used as the input of the proposed hybrid-net, and the interferogram of the third wavelength is used as the output. Using the advantages of the hybrid learning network, the interferogram of the third wavelength can be obtained accurately. Finally, the three-wavelength phase-shifting interferometry is realized. Compared with the previous DL-based dual-wavelength interferometry (DWI), the proposed method can further improve the measurement range of the sample without changing the DWI system. Especially for the independent step sample, the problem of limited measurement range is solved due to the input of auxiliary information. More importantly, the third wavelength can be set freely according to the measurement requirements, which is no longer limited by the actual laser and can provide more measuring ruler for phase measurement. Both experimental results and simulation analysis demonstrate the proposed method in the feasibility and the performance in improving the measurement range.