  We propose a diffractive neural network with strong robustness based on
Weight Noise Injection training, which achieves accurate and fast optical-based
classification while diffraction layers have a certain amount of surface shape
error. To the best of our knowledge, it is the first time that using injection
weight noise during training to reduce the impact of external interference on
deep learning inference results. In the proposed method, the diffractive neural
network learns the mapping between the input image and the label in Weight
Noise Injection mode, making the network's weight insensitive to modest
changes, which improve the network's noise resistance at a lower cost. By
comparing the accuracy of the network under different noise, it is verified
that the proposed network (SRNN) still maintains a higher accuracy under
serious noise.
