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An efficient B-spline curvature-based reflector shape synthesis technique for generating a continental US (CONUS) beam pattern is presented. The approach is based on representing a reflector as a set of curves that can be modified to alter the reflector’s shape. Instead of directly optimizing the curves as done in other commercial software packages, the curvature is evaluated and represented using B-spline polynomials. The control points of each spline curve are defined as optimization variables, which generate new curvature profiles at each iteration. The set of curves with the corresponding curvature is synthesized using the natural equation method and is then used to construct the reflector surface. This two-step process of first optimizing the curvature and then reconstructing a reflector surface greatly facilitates the ability to impose constraints on surface curvature arising from the choice of the manufacturing process. Furthermore, no significant degradation in the convergence behavior was observed when including curvature bounds in the cost function. This makes the approach very attractive when compared to existing methods. In addition, this method can be applied to arbitrary reflector configurations of interest and be paired with any optimization method. The accuracy and validity of the proposed method are verified by multiple application examples.

Antenna array failures have been investigated since the early 1990s, often making use of array factor (AF) theory to accelerate the analysis. However, AF modeling is not always appropriate for reflectarray antennas (RAs) due to their operation in a scattering mode rather than being a driven antenna. Therefore, full-wave analysis is required to model RAs with failures. Unfortunately, this places a tremendous computational burden on any attempt at reoptimizing the RA to heal its performance. To overcome this issue, we introduce here an efficient method that exploits adjoint optimization (AO) to make RA healing tractable. To demonstrate the approach, a $25\times25$ element RA is analyzed under multiple failure scenarios to determine the expected gain recovery after optimization. The proposed optimization framework achieves a $550\times $ speed improvement over conventional approaches, thus making optimization-based RA healing tractable.

Coding metasurfaces (CMSs) rely on the proper tiling of two or more kinds of complementary unit cells within an array to enable phase cancellation and corresponding destructive interference thereby reducing the radar cross-section (RCS) of a given scatterer. Typically, these unit cells are planar and inherently limited in their field of view (FoV). This work demonstrates that breaking this traditional unit cell design paradigm in favor of novel 3-D meta-volumes enables both broadband and wide-FoV RCS reduction. Further, this work introduces a modified genetic algorithm (GA) framework for simultaneously tailoring the reflection phase of multiple unit cells to achieve the best composite response. By applying these identical pair-optimization strategies, it is shown that 3-D unit cells outperform their 2-D counterparts of the same lateral size in terms of both the fractional bandwidth (FBW) and FoV. One exemplary 3-D solution purely improves the $H_{\mathrm {x}}$ response with FBW exceeding 75% across a full 70° FoV. A second 3-D solution provides an improved $E_{\mathrm {x}}$ polarization response with a near 70% FBW at 45° and up to 75% FBW at 70° for the $H_{\mathrm {x}}$ polarization at the expense of reduced FBW close to 50% for incidence angles less than 45°.

Photonic engineered materials have benefitted in recent years from exciting developments in computational electromagnetics and inverse-design tools. However, a commonly encountered issue is that highly performant and structurally complex functional materials found through inverse-design can lose significant performance upon being fabricated. This work introduces a method using deep learning (DL) to exhaustively analyze how structural issues affect the robustness of metasurface supercells, and we show how systems can be designed to guarantee significantly better performance. Moreover, we show that an exhaustive study of structural error is required to make strong guarantees about the performance of engineered materials. The introduction of DL into the inverse-design process makes this problem tractable, enabling optimization runtimes to be measurable in days rather than months and allowing designers to establish exhaustive metasurface robustness guarantees.

Optical metasurfaces, planar subwavelength nanoantenna arrays with the singular ability to sculpt wavefront in almost arbitrary manners, are poised to become a powerful tool enabling compact and high-performance optics with novel functionalities. A particularly intriguing research direction within this field is active metasurfaces, whose optical response can be dynamically tuned postfabrication, thus allowing a plurality of applications unattainable with traditional bulk optics. Designing reconfigurable optics based on active metasurfaces is, however, presented with a unique challenge, since the optical quality of the devices must be optimized at multiple optical states. In this article, we provide a critical review on the active meta-optics design principles and algorithms that are applied across structural hierarchies ranging from single meta-atoms to full meta-optical devices. The discussed approaches are illustrated by specific examples of reconfigurable metasurfaces based on optical phase-change materials.

Recent developments in transformation optics have led to burgeoning research on gradient index lenses for novel optical systems. Such lenses hold great potential for the advancement of complex optics for a wide range of applications. Despite the plethora of literature on gradient index lenses, previous works have not yet considered the application of anti-reflective coatings to these systems. Reducing system reflections is crucial to the development of this technology for highly sensitive optical applications. Here, we present effective anti-reflective-coating designs for gradient index lens systems. Conventional anti-reflective-design methodologies are leveraged in conjunction with transformation optics to develop coatings that significantly reduce reflections of a flat gradient index lens. Finally, the resulting gradient-index anti-reflective coatings are compared and contrasted with conventional homogeneous anti-reflective coatings.