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Ghost Diffractive Deep Neural Networks: Optical Classifications Using Light’s Second-Order Coherence

Zhiyuan Ye, Chenjie Zhou, Chen-Xin Ding, Jilun Zhao,Shuming Jiao,Hai-Bo Wang,Jun Xiong

PHYSICAL REVIEW APPLIED(2023)

Beijing Normal Univ

Cited 4|Views11
Abstract
Since Hanbury Brown and Twiss proposed intensity interferometry in 1956, light's fluctuating nature, high-order coherence, and spatial correlations have become not only the cornerstones of quantum optics but also resources for many classical optical applications. Correlation-based optical metrologies, including ghost imaging and ghost diffraction, have distinct advantages ranging from local to nonlocal geometry, spatially coherent to incoherent light, and array to single-pixel sampling. In this paper we propose ghost diffractive deep neural networks (GD2NNs), a nonlocal optical information-processing system that combines traditional ghost diffraction with cascaded diffraction layers "learned" with use of diffractive deep neural networks. GD2NNs use light's second-order coherence to enable image-free and interferometerfree coherent beam-demanded phase-object sorting with thermal light. Furthermore, GD2NNs convert the general encoder-decoder-detector cascaded framework into a parallel one, resulting in no optical interaction between the encoder and the decoder. As a proof-of-principle demonstration, we numerically and experimentally classify different phase objects using three-layer and two-layer GD2NNs, respectively. This paper effectively provides a paradigm shift, particularly for diffraction-related coherent linear optical information processing systems, from spatially coherent to incoherent light and from cascaded to parallel processing.
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Key words
Ghost Imaging,Diffractive Optical Neural Networks,Light Propagation,Deep Learning,Structured Light
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