  Self mixing interferometry is a well established interferometric measurement
technique. In spite of the robustness and simplicity of the concept,
interpreting the self-mixing signal is often complicated in practice, which is
detrimental to measurement availability. Here we discuss the use of a
convolutional neural network to reconstruct the displacement of a target from
the self mixing signal in a semiconductor laser. The network, once trained on
periodic displacement patterns, can reconstruct arbitrarily complex
displacement in different alignment conditions and setups. The approach
validated here is amenable to generalization to modulated schemes or even to
totally different self mixing sensing tasks.
