  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.
