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We analyze the information processing capacity of coherent optical networks formed by trainable diffractive surfaces to prove that the dimensionality of the solution space describing the set of all-optical transformations established by a diffractive network increases linearly with the number of diffractive surfaces, up to a limit determined by the size of the input/output fields-of-view. Deeper diffractive networks formed by larger numbers of trainable diffractive surfaces span a broader subspace of the complex-valued transformations between larger input/output fields-of-view, and present major advantages in terms of their function approximation power, inference accuracy and learning/generalization capabilities compared to a single diffractive surface.
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Onur Kulce, Deniz Mengu, Yair Rivenson, Aydogan Ozcan, "Information processing capacity of diffractive surfaces," Proc. SPIE 11703, AI and Optical Data Sciences II, 1170310 (5 March 2021); https://doi.org/10.1117/12.2580540