Gao Xinxin, Gu Ze, Ma Qian, Chen Bao Jie, Shum Kam-Man, Cui Wen Yi, You Jian Wei, Cui Tie Jun, Chan Chi Hou
State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong, China.
State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China.
Nat Commun. 2024 Aug 6;15(1):6686. doi: 10.1038/s41467-024-51210-2.
All-optical diffractive neural networks, as analog artificial intelligence accelerators, leverage parallelism and analog computation for complex data processing. However, their low space transmission efficiency or large spatial dimensions hinder miniaturization and broader application. Here, we propose a terahertz spoof plasmonic neural network on a planar diffractive platform for direct multi-target recognition. Our approach employs a spoof surface plasmon polariton coupler array to construct a diffractive network layer, resulting in a compact, efficient, and easily integrable architecture. We designed three schemes: basis vector classification, multi-user recognition, and MNIST handwritten digit classification. Experimental results reveal that the terahertz spoof plasmonic neural network successfully classifies basis vectors, recognizes multi-user orientation information, and directly processes handwritten digits using a designed input framework comprising a metal grating array, transmitters, and receivers. This work broadens the application of terahertz plasmonic metamaterials, paving the way for terahertz on-chip integration, intelligent communication, and advanced computing systems.
全光衍射神经网络作为模拟人工智能加速器，利用并行性和模拟计算进行复杂数据处理。然而，它们较低的空间传输效率或较大的空间尺寸阻碍了小型化和更广泛的应用。在此，我们提出了一种基于平面衍射平台的太赫兹仿表面等离激元神经网络，用于直接多目标识别。我们的方法采用仿表面等离激元极化子耦合器阵列来构建衍射网络层，从而得到一种紧凑、高效且易于集成的架构。我们设计了三种方案：基向量分类、多用户识别和MNIST手写数字分类。实验结果表明，太赫兹仿表面等离激元神经网络利用包含金属光栅阵列、发射器和接收器的设计输入框架，成功地对基向量进行了分类，识别了多用户方向信息，并直接处理了手写数字。这项工作拓宽了太赫兹等离激元超材料的应用，为太赫兹片上集成、智能通信和先进计算系统铺平了道路。
