Optical shaping self-mixing interferometry with a neural network for displacement measurement

Based on the characteristics of optical shaping self-mixing interference (SMI) and its perfect alignment with the input requirements of neural networks (NNs) for phase extraction, a novel, to our knowledge, displacement measurement method is proposed in this work. Optical shaping involves using a static Fabry-Perot cavity to map the periodic variations of optical frequency generated by SMI, achieving fringe multiplication, signal normalization, and enhancement for SMI optically. ANN trained on simulated data is used to directly extract the phase from the spectrum-mapped SMI signal. This measurement technology achieves a relative accuracy of 10-3 and advances the development of SMI. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.