失调弹性衍射光网络
Misalignment Resilient Diffractive Optical Networks
摘要：
作为一种光学机器学习框架，衍射深度神经网络（D2 NN）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断任务。利用该衍射框架设计的多层光学对象识别平台已经被示出为推广到不可见的图像数据，手写数字分类的盲推理准确率>98%。衍射网络的多层结构在其衍射效率、推理能力和光学信号对比度方面提供了显著的优势。然而，多个衍射层的使用也给这些衍射系统的制造和对准带来了实际挑战，以实现准确的光学推理。在这里，我们介绍和实验证明了一种新的训练方案，显着增加了衍射网络的鲁棒性对3D错位和制造公差的物理实现的训练衍射网络。通过在光学前向模型中将3D中不期望的层间错位建模为连续随机变量，衍射网络被训练以在大范围的错位中保持其推理精度;我们将这种衍射网络设计称为接种D2 NN（v-D2 NN）。我们进一步将这种疫苗接种策略扩展到在输出平面使用差分检测器的衍射网络的训练以及联合训练的混合（光电）网络，以揭示所有这些衍射设计通过考虑可能的3D制造变化和位移来提高其对未对准的弹性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
