Plasmonic nanoantennas with suitable far-field characteristics are of huge interest for utilization in optical wireless links, inter-/intrachip communications, LiDARs, and photonic integrated circuits due to their exceptional modal confinement. Despite its success in shaping robust antenna design theories in radio frequency and millimeter-wave regimes, conventional transmission line theory finds its validity diminished in the optical frequencies, leading to a noticeable void in a generalized theory for antenna design in the optical domain. By utilizing neural networks, and through a one-time training of the network, one can transform the plasmonic nanoantennas design into an automated, data-driven task. In this work, we have developed a multi-head deep convolutional neural network serving as an efficient inverse-design framework for plasmonic patch nanoantennas. Our framework is designed with the main goal of determining the optimal geometries of nanoantennas to achieve the desired (inquired by the designer) S 11 and radiation pattern simultaneously. The proposed approach preserves the one-to-many mappings, enabling us to generate diverse designs. In addition, apart from the primary fabrication limitations that were considered while generating the dataset, further design and fabrication constraints can also be applied after the training process. In addition to possessing an exceptionally rapid surrogate solver capable of predicting S 11 and radiation patterns throughout the entire design frequency spectrum, we are introducing what we believe to be the pioneering inverse design network. This network enables the creation of efficient plasmonic antennas while concurrently accommodating customizable queries for both S 11 and radiation patterns, achieving remarkable accuracy within a single network framework. Our framework is capable of designing a wide range of devices, including single band, dual band, and broadband antennas, with directivities and radiation efficiencies reaching 11.07 dBi and 75 %, respectively, for a single patch. The proposed approach has been developed as a transformative shift in the inverse design of photonics components, with its impact extending beyond antenna design, opening a new paradigm toward real-time design of application-specific nanophotonic devices.

中文翻译：

---

集成深度卷积代理求解器和粒子群优化，实现等离子体贴片纳米天线的高效逆向设计


具有合适远场特性的等离子体纳米天线由于其特殊的模态限制，在光学无线链路、芯片间/芯片内通信、激光雷达和光子集成电路中的应用引起了极大的兴趣。尽管传统传输线理论成功地塑造了射频和毫米波领域的稳健天线设计理论，但传统传输线理论发现其有效性在光频率中减弱，导致光域天线设计的广义理论存在明显的空白。通过利用神经网络并通过网络的一次性训练，人们可以将等离子体纳米天线设计转变为一项自动化的数据驱动任务。在这项工作中，我们开发了一种多头深度卷积神经网络，作为等离子体贴片纳米天线的有效逆向设计框架。我们框架的设计主要目标是确定纳米天线的最佳几何形状以实现所需的效果（由设计者询问） S 11和辐射方向图同时进行。所提出的方法保留了一对多映射，使我们能够生成不同的设计。此外，除了生成数据集时考虑的主要制造限制之外，在训练过程后还可以应用进一步的设计和制造约束。除了拥有能够预测的异常快速的代理求解器S 11和整个设计频谱的辐射模式，我们正在推出我们认为是开创性的逆向设计网络。 该网络能够创建高效的等离激元天线，同时满足两者的可定制查询S 11和辐射模式，在单一网络框架内实现卓越的准确性。我们的框架能够设计各种设备，包括单频段、双频段和宽带天线，单个贴片的方向性和辐射效率分别达到 11.07 dBi 和 75%。所提出的方法已被开发为光子元件逆向设计的变革性转变，其影响超出了天线设计，为特定应用的纳米光子器件的实时设计开辟了新的范例。