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Machine learning assisted control of integrated optical phased arrays

We propose a Machine Learning (ML) based approach to calculate the control signals to apply to an integrated Optical Phased Array (OPA) to get the desired far field profile, which is of particular interest for single-pixel imaging applications. We validated this approach considering a 1D OPA with 8 thermally controlled input waveguides and an 8x1 combiner. We generated 7500 random combinations of the 8 control signals, performing BPM simulations in Synopsys RSoft to calculate far field intensity maps later used to train in MATLAB the ML algorithm. The trained network can then generate the control signals required to obtain any desired far field pattern.