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This article presents a novel method for the forward modeling and inverse design of a class of Schiffman phase shifters using deep neural networks (DNNs). Since DNNs are capable of mapping the highly nonlinear correlations between inputs and outputs, we constructed a fully connected DNN to predict the electromagnetic (EM) responses of Schiffman phase shifters given their physical dimensions. Based on this fast and accurate modeling tool, a cascaded inverse design DNN was then built and trained to achieve instant on-demand phase shifter designs. This approach is versatile and can be easily modified to accomplish different design goals. To demonstrate its efficacy, we trained two DNNs to realize Schiffman phase shifter designs with different bandwidths (40% and 60%) and arbitrary phase shift targets (0°–180°). Simulation results and experimental verifications substantiate that their performances are comparable with the state-of-the-art designs. Moreover, we discussed the proposed methods’ potential in dealing with design tasks that are nonintuitive and beyond the scope of the existing approaches. We envision that this DNN approach can be extended to the design of various EM components including but not limited to antennas, filters, power dividers, and frequency selective surfaces (FSS). [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep learning approach for forward modeling and inverse design of photonic devices containing embedded active metasurface structures. In particular, we demonstrate that combining neural network design of metasurfaces with scattering matrix-based optimization significantly simplifies the computational overhead while facilitating accurate objective-driven design. As an example, we apply our approach to the design of a continuously tunable bandpass filter in the mid-wave infrared, featuring narrow passband (∼10 nm), high quality factors (Q-factors ∼ 102), and large out-of-band rejection (optical density ≥ 3). The design consists of an optical phase-change material Ge2Sb2Se4Te (GSST) metasurface atop a silicon heater sandwiched between two distributed Bragg reflectors (DBRs). The proposed design approach can be generalized to the modeling and inverse design of arbitrary response photonic devices incorporating active metasurfaces. [ABSTRACT FROM AUTHOR]

Metasurfaces have provided a novel and promising platform for realizing compact and high‐performance optical devices. The conventional metasurface design approach assumes periodic boundary conditions for each element, which is inaccurate in most cases since near‐field coupling effects between elements will change when the element is surrounded by nonidentical structures. In this paper, a deep learning approach is proposed to predict the actual electromagnetic (EM) responses of each target meta‐atom placed in a large array with near‐field coupling effects taken into account. The predicting neural network takes the physical specifications of the target meta‐atom and its neighbors as input, and calculates its actual phase and amplitude in milliseconds. This approach can be used to optimize metasurfaces' efficiencies when combined with optimization algorithms. To demonstrate the efficacy of this methodology, large improvements in efficiency for a beam deflector and a metalens over the conventional design approach are obtained. Moreover, it is shown that the correlations between a metasurface's performance and its design errors caused by mutual coupling are not bound to certain specifications (materials, shapes, etc.). As such, it is envisioned that this approach can be readily applied to explore the mutual coupling effects and improve the performance of various metasurface designs. [ABSTRACT FROM AUTHOR]

Metasurfaces have enabled precise electromagnetic (EM) wave manipulation with strong potential to obtain unprecedented functionalities and multifunctional behavior in flat optical devices. These advantages in precision and functionality come at the cost of tremendous difficulty in finding individual meta‐atom structures based on specific requirements (commonly formulated in terms of EM responses), which makes the design of multifunctional metasurfaces a key challenge in this field. In this paper, a generative adversarial network that can tackle this problem and generate meta‐atom/metasurface designs to meet multifunctional design goals is presented. Unlike conventional trial‐and‐error or iterative optimization design methods, this new methodology produces on‐demand free‐form structures involving only a single design iteration. More importantly, the network structure and the robust training process are independent of the complexity of design objectives, making this approach ideal for multifunctional device design. Additionally, the ability of the network to generate distinct classes of structures with similar EM responses but different physical features can provide added latitude to accommodate other considerations such as fabrication constraints and tolerances. The network's ability to produce a variety of multifunctional metasurface designs is demonstrated by presenting a bifocal metalens, a polarization‐multiplexed beam deflector, a polarization‐multiplexed metalens, and a polarization‐independent metalens. [ABSTRACT FROM AUTHOR]