We analyze the information processing capacity of diffractive optical networks to reveal that increasing the total number of diffractive features, i.e., neurons, within a network linearly increases the dimensionality of the complex-valued linear transformation space of the network, up to a limit dictated by the input and output fields-of-view. We further show that deeper diffractive neural networks formed by larger numbers of diffractive surfaces can cover a higher-dimensional subspace of the complex-valued linear transformations between a larger input field-of-view and a larger output field-of-view, increasing the learning capability and approximation power of the optical network.
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