Abstract
Modern raster scanning techniques and single pixel applications require a precise control of the field profiles radiated by Optical Phased Arrays, which can be controlled by external electrical signals modifying the optical paths inside these devices. In particular for single pixel imaging solutions, it is generally not straightforward to identify the control signals required to generate the desired far field pattern. We propose therefore a Convolutional Neural Network based approach which allows a user to determine the signals required to accurately reproduce an arbitrary far field profile.
Highlights
I N the last years, Optical Phased Arrays (OPAs) have gained popularity in a variety of application fields where an integrated, non-mechanical solution is required to precisely control the direction of an optical beam
To what happens in microwave phased arrays, OPAs can be seen as optical antennas where the output beam is controlled electrically by operating on the optical path of integrated optical components
Thanks to their beam steering capability, together with their reduced weight and dimensions, OPAs find natural application in all the fields where the traditional bulky beam steering systems composed by mechanical servo and optical lens cannot meet the requirements, with applications ranging from inter-satellite communication and sensors to autonomous vehicles [1]–[7]
Summary
I N the last years, Optical Phased Arrays (OPAs) have gained popularity in a variety of application fields where an integrated, non-mechanical solution is required to precisely control the direction of an optical beam. Whichever paradigm or technology is used to develop the desired OPA device, a crucial block in the device operation is the phase control region In particular this feature can be implemented resorting to many different well established techniques, for example electrical and thermal control. Inverse design algorithms are widely exploited to optimize the component characteristics and are able to find non-intuitive and irregular solutions that can outperform the analytically or empirically designed topologies These approaches are widely used for the characterization of the device’s behavior since the simulations of photonic components based on the common Beam Propagation Method (BPM) or Finite Difference Time Domain (FDTD) schemes are generally extremely time consuming: on the contrary a properly trained machine can reasonably approximate the analyzed behavior in fractions of a second. This approach can be immediately extended to a more complicated 2D case, at the cost of a longer total simulation time, due to the larger number of control signals required
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