Compact planar-waveguide integrated diffractive optical neural network chip

This is the authors' abstract. We don't add key points for this paper.

Diffractive optical neural networks (DONNs) have exhibited the advantages of parallelization, high speed, and low consumption. However, the existing DONNs based on free-space diffractive optical elements are bulky and unsteady. In this study, we propose a planar-waveguide integrated diffractive neural network chip architecture. The three diffractive layers are engraved on the same side of a quartz wafer. The three-layer chip is designed with 32-mm3 processing space and enables a computing speed of 3.1×109 Tera operations per second. The results show that the proposed chip achieves 73.4% experimental accuracy for the Modified National Institute of Standards and Technology database while showing the system’s robustness in a cycle test. The consistency of experiments is 88.6%, and the arithmetic mean standard deviation of the results is ~4.7%. The proposed chip architecture can potentially revolutionize high-resolution optical processing tasks with high robustness.