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Sub-wavelength diffractive meta-optics have emerged as a versatile platform to manipulate light fields at will, due to their ultra-small form factor and flexible multifunctionalities. However, miniaturization and multimodality are typically compromised by a reduction in imaging performance; thus, meta-optics often yield lower resolution and stronger aberration compared to traditional refractive optics. Concurrently, computational approaches have become popular to improve the image quality of traditional cameras and exceed limitations posed by refractive lenses. This in turn often comes at the expense of higher power and latency, and such systems are typically limited by the availability of certain refractive optics. Limitations in both fields have thus sparked cross-disciplinary efforts to not only overcome these roadblocks but also to go beyond and provide synergistic meta-optical–digital solutions that surpass the potential of the individual components. For instance, an application-specific meta-optical frontend can preprocess the light field of a scene and focus it onto the sensor with a desired encoding, which can either ease the computational load on the digital backend or can intentionally alleviate certain meta-optical aberrations. In this review, we introduce the fundamentals, summarize the development of meta-optical computational imaging, focus on latest advancements that redefine the current state of the art, and give a perspective on research directions that leverage the full potential of sub-wavelength photonic platforms in imaging and sensing applications. The current advancement of meta-optics and recent investments by foundries and technology partners have the potential to provide synergistic future solutions for highly efficient, compact, and low-power imaging systems. 

Abstract Silicon is a common material of choice for semiconductor optics in the infrared spectral range, due to its low cost, well-developed high-volume manufacturing methods, high refractive index, and transparency. It is, however, typically ill-suited for applications in the visible range, due to its large absorption coefficient, especially for green and blue light. Counterintuitively, we demonstrate how ultra-thin crystalline meta-optics enable full-color imaging in the visible range. For this purpose, we employ an inverse design approach, which maximizes the volume under the broadband modulation transfer function of the meta-optics. Beyond that, we demonstrate polarization-multiplexed functionality in the visible. This is particularly important as polarization optics require high index materials, a characteristic often difficult to obtain in the visible. 

Rapid advancements in autonomous systems and the Internet of Things have necessitated the development of compact and low-power image sensors to bridge the gap between the digital and physical world. To that end, sub-wavelength diffractive optics, commonly known as meta-optics, have garnered significant interest from the optics and photonics community due to their ability to achieve multiple functionalities within a small form factor. Despite years of research, however, the performance of meta-optics has often remained inferior compared to that of traditional refractive optics. In parallel, computational imaging techniques have emerged as a promising path to miniaturize optical systems, albeit often at the expense of higher power and latency. The lack of desired performance from either meta-optical or computational solutions has motivated researchers to look into a jointly optimized meta-optical–digital solution. While the meta-optical front end can preprocess the scene to reduce the computational load on the digital back end, the computational back end can in turn relax requirements on the meta-optics. In this Perspective, we provide an overview of this up-and-coming field, termed here as “software-defined meta-optics.” We highlight recent contributions that have advanced the current state of the art and point out directions toward which future research efforts should be directed to leverage the full potential of subwavelength photonic platforms in imaging and sensing applications. Synergistic technology transfer and commercialization of meta-optic technologies will pave the way for highly efficient, compact, and low-power imaging systems of the future. 

Abstract Miniature varifocal lenses are crucial for many applications requiring compact optical systems. Here, utilizing electro-mechanically actuated 0.5-mm aperture infrared Alvarez meta-optics, we demonstrate 3.1 mm (200 diopters) focal length tuning with an actuation voltage below 40 V. This constitutes the largest focal length tuning in any low-power electro-mechanically actuated meta-optic, enabled by the high energy density in comb-drive actuators producing large displacements at relatively low voltage. The demonstrated device is produced by a novel nanofabrication process that accommodates meta-optics with a larger aperture and has improved alignment between meta-optics via flip-chip bonding. The whole fabrication process is CMOS compatible and amenable to high-throughput manufacturing. 

Abstract Endoscopes are an important component for the development of minimally invasive surgeries. Their size is one of the most critical aspects, because smaller and less rigid endoscopes enable higher agility, facilitate larger accessibility, and induce less stress on the surrounding tissue. In all existing endoscopes, the size of the optics poses a major limitation in miniaturization of the imaging system. Not only is making small optics difficult, but their performance also degrades with downscaling. Meta-optics have recently emerged as a promising candidate to drastically miniaturize optics while achieving similar functionalities with significantly reduced size. Herein, we report an inverse-designed meta-optic, which combined with a coherent fiber bundle enables a 33% reduction in the rigid tip length over traditional gradient-index (GRIN) lenses. We use the meta-optic fiber endoscope (MOFIE) to demonstrate real-time video capture in full visible color, the spatial resolution of which is primarily limited by the fiber itself. Our work shows the potential of meta-optics for integration and miniaturization of biomedical devices towards minimally invasive surgery. 

Abstract We report an inverse-designed, high numerical aperture (∼0.44), extended depth of focus (EDOF) meta-optic, which exhibits a lens-like point spread function (PSF). The EDOF meta-optic maintains a focusing efficiency comparable to that of a hyperboloid metalens throughout its depth of focus. Exploiting the extended depth of focus and computational post processing, we demonstrate broadband imaging across the full visible spectrum using a 1 mm, f/1 meta-optic. Unlike other canonical EDOF meta-optics, characterized by phase masks such as a log-asphere or cubic function, our design exhibits a highly invariant PSF across ∼290 nm optical bandwidth, which leads to significantly improved image quality, as quantified by structural similarity metrics. 

Extended depth of focus (EDOF) optics can enable lower complexity optical imaging systems when compared to active focusing solutions. With existing EDOF optics, however, it is difficult to achieve high resolution and high collection efficiency simultaneously. The subwavelength spacing of scatterers in a meta-optic enables the engineering of very steep phase gradients; thus, meta-optics can achieve both a large physical aperture and a high numerical aperture. Here, we demonstrate a fast $(f/1.75)$EDOF meta-optic operating at visible wavelengths, with an aperture of 2 mm and focal range from 3.5 mm to 14.5 mm (286 diopters to 69 diopters), which is a $250\times$elongation of the depth of focus relative to a standard lens. Depth-independent performance is shown by imaging at a range of finite conjugates, with a minimum spatial resolution of $\sim 9.84\mathrm{\mu m}$(50.8 cycles/mm). We also demonstrate operation of a directly integrated EDOF meta-optic camera module to evaluate imaging at multiple object distances, a functionality which would otherwise require a varifocal lens. 

Abstract Ultrathin meta-optics offer unmatched, multifunctional control of light. Next-generation optical technologies, however, demand unprecedented performance. This will likely require design algorithms surpassing the capability of human intuition. For the adjoint method, this requires explicitly deriving gradients, which is sometimes challenging for certain photonics problems. Existing techniques also comprise a patchwork of application-specific algorithms, each focused in scope and scatterer type. Here, we leverage algorithmic differentiation as used in artificial neural networks, treating photonic design parameters as trainable weights, optical sources as inputs, and encapsulating device performance in the loss function. By solving a complex, degenerate eigenproblem and formulating rigorous coupled-wave analysis as a computational graph, we support both arbitrary, parameterized scatterers and topology optimization. With iteration times below the cost of two forward simulations typical of adjoint methods, we generate multilayer, multifunctional, and aperiodic meta-optics. As an open-source platform adaptable to other algorithms and problems, we enable fast and flexible meta-optical design.