Efficient Inverse Design of Plasmonic Patch Nanoantennas using Deep Learning

TL;DR

This paper presents a deep learning-based inverse design framework for plasmonic patch nanoantennas, enabling rapid, diverse, and optimized geometries to achieve specific optical properties, addressing the lack of a generalized optical antenna theory.

Contribution

The work introduces a multi-head deep convolutional neural network for inverse design of nanoantennas, capable of generating diverse optimal geometries considering multiple constraints.

Findings

Rapid prediction of S11 and radiation patterns across frequencies.

Generation of diverse nanoantenna designs for specified optical properties.

Framework accommodates additional fabrication constraints post-training.

Abstract

Plasmonic nanoantennas with suitable far-field characteristics are of huge interest for utilization in optical wireless links, inter-/intra-chip 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.