Diffractive deep neural networks (D2NNs) have garnered significant attention for their ultra-low energy consumption and parallel optical computing capabilities. However, their practical deployment is hindered by the “model–reality” gap caused by fabrication inaccuracy, device fluctuation, assembly misalignment, environmental perturbation, etc. Here, we propose a combined framework that integrates sharpness-aware minimization (SAM) and aberration-immune learning (AIL), enabling joint immunity against both stochastic noise and systematic deviations from theoretical model training. Specifically, we show that under multiple perturbations such as salt-and-pepper noise, Gaussian noise, and wavefront aberration, the SAM–AIL framework achieves significant classification accuracy improvements on MNIST and Fashion-MNIST compared to conventional offline training approaches. D2NN trained with the SAM–AIL scheme exhibited significant accuracy enhancement under moderate salt-and-pepper noise, Gaussian noise, X-axis, and Y-axis tilting perturbations, respectively. Our work provides an efficient solution for offline training and deploying high-robustness D2NNs on realistic physical systems that are resilient to a variety of imperfections, significantly enhancing model transferability and reliability for optical computing tasks.
Li et al. (Sat,) studied this question.