The electromagnetic response of plasmonic nanostructures is highly sensitive to their geometric parameters. In multi-dimensional parameter space, conventional full-wave simulation and numerical optimization can consume significant computation time and resources. It is also highly challenging to find the globally optimized result and perform inverse design for a highly nonlinear data structure. In this work, we demonstrate that a simple multi-layer perceptron deep neural network can capture the highly nonlinear, complex relationship between plasmonic geometry and its near- and far-field properties. Our deep learning approach proves accurate inverse design of near-field enhancement and far-field spectrum simultaneously, which can enable the design of dual-functional optical sensors. Such implementation is helpful for exploring subtle, complex multifunctional nanophotonics for sensing and energy conversion applications.

中文翻译：

---

用于设计等离子体天线近场和远场特性的深度神经网络

等离子体纳米结构的电磁响应对其几何参数高度敏感。在多维参数空间中，传统的全波模拟和数值优化会消耗大量的计算时间和资源。对于高度非线性的数据结构，找到全局优化的结果并进行逆向设计也是非常具有挑战性的。在这项工作中，我们证明了一个简单的多层感知器深度神经网络可以捕捉等离子体几何与其近场和远场特性之间的高度非线性、复杂的关系。我们的深度学习方法同时证明了近场增强和远场频谱的准确逆向设计，可以实现双功能光学传感器的设计。这样的实现有助于探索微妙的，