Information processing capacity of diffractive surfaces

Onur Kulce; Deniz Mengu; Yair Rivenson; Aydogan Ozcan

doi:10.1117/12.2580540

5 March 2021 Information processing capacity of diffractive surfaces

Onur Kulce, Deniz Mengu, Yair Rivenson, Aydogan Ozcan

Proceedings Volume 11703, AI and Optical Data Sciences II; 1170310 (2021) https://doi.org/10.1117/12.2580540
Event: SPIE OPTO, 2021, Online Only

Abstract

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, and 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

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