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We propose a diffractive neural network with strong robustness based onWeight Noise Injection training, which achieves accurate and fast optical-basedclassification while diffraction layers have a certain amount of surface shapeerror. To the best of our knowledge, it is the first time that using injectionweight noise during training to reduce the impact of external interference ondeep learning inference results. In the proposed method, the diffractive neuralnetwork learns the mapping between the input image and the label in WeightNoise Injection mode, making the network's weight insensitive to modestchanges, which improve the network's noise resistance at a lower cost. Bycomparing the accuracy of the network under different noise, it is verifiedthat the proposed network (SRNN) still maintains a higher accuracy underserious noise.

A Diffractive Neural Network with Weight-Noise-Injection Training