Ghost diffractive deep neural networks: Optical classifications using light’s second-order coherence
Zhiyuan Ye, Chenjie Zhou, Chen‐Xin Ding, Jilun Zhao, Shuming Jiao, Haibo Wang, Jun Xiong
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 (${\mathrm{GD}}^{2}\mathrm{NNs}$), a nonlocal optical information-processing system that combines traditional ghost diffraction with cascaded diffraction layers ``learned'' with use of diffractive deep neural networks. ${\mathrm{GD}}^{2}\mathrm{NNs}$ use light's second-order coherence to enable image-free and interferometer-free coherent beam-demanded phase-object sorting with thermal light. Furthermore, ${\mathrm{GD}}^{2}\mathrm{NNs}$ 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 ${\mathrm{GD}}^{2}\mathrm{NNs}$, 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.