提出了一种基于10.6 μm波长的小型化非线性全光衍射深度神经网络建模方法。采用波长为10.6 μm的二氧化碳(CO₂)激光光源,其对应的神经网络物理尺寸为1 mm×1 mm,依据相关的光学物理参数特性,构建了基于10.6 μm波长的非线性全光衍射深度神经网络模型框架,使用网格搜索法确定最优的神经网络模型超参数,并选择交叉熵损失函数和Adam优化器对神经网络进行了优化。分别在MNIST手写数字数据集和Fashion-MNIST数据集上对该方法进行了测试,其分类结果分别达到了0.9630和0.8743。所提方法为制备小型化的全光衍射光栅提供了理论参考。

One method used for modeling a miniaturized nonlinear all-optical diffraction deep neural network based on 10.6 μm wavelength is proposed. First, a carbon dioxide (CO₂) laser light source with a wavelength of 10.6 μm is used, and the corresponding physical size of the neural network is 1 mm×1 mm. Second, the model framework of the nonlinear all-optical diffraction deep neural network based on 10.6 μm wavelength is constructed according to the characteristics of relevant optical physical parameters. Finally, the grid search method is used to determine the hyper-parameters of the optimal neural network model, and the cross entropy loss function and the Adam optimizer are selected to optimize the neural network. The proposed method is tested on the MNIST handwritten digital dataset and the Fashion-MNIST dataset, respectively, and the classification results reach 0.9630 and 0.8743, respectively. The proposed method provides theoretical reference for the preparation of miniaturized all-optical diffraction gratings.