Optimize performance of a diffractive neural network by controlling the Fresnel number

Abstract

To achieve better performance of a diffractive deep neural network, increasing its spatial complexity (neurons and layers) is commonly used. Subject to physical laws of optical diffraction, a deeper diffractive neural network (DNN) would be more difficult to implement, and the development of DNN is limited. In this work, we found controlling the Fresnel number can increase DNN’s capability of expression and its spatial complexity is even less. DNN with only one phase modulation layer was proposed and experimentally realized at 515 nm. With the optimal Fresnel number, the single-layer DNN reached a maximum accuracy of 97.08% in the handwritten digits recognition task.