Gu Ze, Ma Qian, Gao Xinxin, You Jian Wei, Cui Tie Jun
Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China.
State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China.
Sci Adv. 2024 Jul 19;10(29):eado3937. doi: 10.1126/sciadv.ado3937.
Diffractive neural network in electromagnetic wave-driven system has attracted great attention due to its ultrahigh parallel computing capability and energy efficiency. However, recent neural networks based on the diffractive framework still face the bottlenecks of misalignment and relatively large size limiting their further applications. Here, we propose a planar diffractive neural network (pla-NN) with a highly integrated and conformal architecture to achieve direct signal processing in the microwave frequency. On the basis of printed circuit fabrication process, the misalignment could be effectively circumvented while enabling flexible extension for multiple conformal and stacking designs. We first conduct validation on the fashion-MNIST dataset and experimentally build up a system using the proposed network architecture for direct recognition of different geometry structures in the electromagnetic space. We envision that the presented architecture, once combined with the advanced dynamic maneuvering techniques and flexible topology, would exhibit unlimited potentials in the areas of high-performance computing, wireless sensing, and flexible wearable electronics.
由于其超高的并行计算能力和能源效率，电磁波驱动系统中的衍射神经网络引起了广泛关注。然而，最近基于衍射框架的神经网络仍然面临着对准误差和尺寸相对较大的瓶颈，限制了它们的进一步应用。在这里，我们提出了一种具有高度集成和共形架构的平面衍射神经网络（pla-NN），以实现微波频率下的直接信号处理。基于印刷电路制造工艺，可以有效规避对准误差，同时实现多种共形和堆叠设计的灵活扩展。我们首先在Fashion-MNIST数据集上进行验证，并通过实验建立了一个使用所提出的网络架构直接识别电磁空间中不同几何结构的系统。我们设想，一旦所提出的架构与先进的动态操纵技术和灵活的拓扑结构相结合，将在高性能计算、无线传感和柔性可穿戴电子等领域展现出无限潜力。
