Abstract As an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light–matter interaction in 3D for performing a desired statistical inference task. Multi-layer optical object recognition platforms designed with this diffractive framework have been shown to generalize to unseen image data achieving, e.g., >98% blind inference accuracy for hand-written digit classification. The multi-layer structure of diffractive networks offers significant advantages in terms of their diffraction efficiency, inference capability and optical signal contrast. However, the use of multiple diffractive layers also brings practical challenges for the fabrication and alignment of these diffractive systems for accurate optical inference. Here, we introduce and experimentally demonstrate a new training scheme that significantly increases the robustness of diffractive networks against 3D misalignments and fabrication tolerances in the physical implementation of a trained diffractive network. By modeling the undesired layer-to-layer misalignments in 3D as continuous random variables in the optical forward model, diffractive networks are trained to maintain their inference accuracy over a large range of misalignments; we term this diffractive network design as vaccinated D2NN (v-D2NN). We further extend this vaccination strategy to the training of diffractive networks that use differential detectors at the output plane as well as to jointly-trained hybrid (optical-electronic) networks to reveal that all of these diffractive designs improve their resilience to misalignments by taking into account possible 3D fabrication variations and displacements during their training phase.

中文翻译：

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未对准弹性衍射光学网络

摘要 作为一种光学机器学习框架，衍射深度神经网络 (D2NN) 利用深度学习中使用的数据驱动训练方法来设计 3D 中的光-物质相互作用，以执行所需的统计推理任务。使用这种衍射框架设计的多层光学对象识别平台已被证明可以推广到看不见的图像数据，例如，手写数字分类的盲推断准确率 >98%。衍射网络的多层结构在衍射效率、推理能力和光信号对比度方面具有显着优势。然而，使用多个衍射层也为这些衍射系统的制造和对准带来了实际挑战，以实现精确的光学推断。这里，我们引入并通过实验证明了一种新的训练方案，该方案显着提高了衍射网络在经过训练的衍射网络的物理实现中对抗 3D 错位和制造公差的鲁棒性。通过将 3D 中不需要的层到层错位建模为光学前向模型中的连续随机变量，训练衍射网络以在大范围错位上保持推理精度；我们将这种衍射网络设计称为接种 D2NN (v-D2NN)。