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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. 

Abstract Subwavelength diffractive optics known as meta-optics have demonstrated the potential to significantly miniaturize imaging systems. However, despite impressive demonstrations, most meta-optical imaging systems suffer from strong chromatic aberrations, limiting their utilities. Here, we employ inverse-design to create broadband meta-optics operating in the long-wave infrared (LWIR) regime (8-12μm). Via a deep-learning assisted multi-scale differentiable framework that links meta-atoms to the phase, we maximize the wavelength-averaged volume under the modulation transfer function (MTF) surface of the meta-optics. Our design framework merges local phase-engineering via meta-atoms and global engineering of the scatterer within a single pipeline. We corroborate our design by fabricating and experimentally characterizing all-silicon LWIR meta-optics. Our engineered meta-optic is complemented by a simple computational backend that dramatically improves the quality of the captured image. We experimentally demonstrate a six-fold improvement of the wavelength-averaged Strehl ratio over the traditional hyperboloid metalens for broadband imaging. 

Abstract Light’s ability to perform massive linear operations in parallel has recently inspired numerous demonstrations of optics-assisted artificial neural networks (ANN). However, a clear system-level advantage of optics over purely digital ANN has not yet been established. While linear operations can indeed be optically performed very efficiently, the lack of nonlinearity and signal regeneration require high-power, low-latency signal transduction between optics and electronics. Additionally, a large power is needed for lasers and photodetectors, which are often neglected in the calculation of the total energy consumption. Here, instead of mapping traditional digital operations to optics, we co-designed a hybrid optical-digital ANN, that operates on incoherent light, and is thus amenable to operations under ambient light. Keeping the latency and power constant between a purely digital ANN and a hybrid optical-digital ANN, we identified a low-power/latency regime, where an optical encoder provides higher classification accuracy than a purely digital ANN. We estimate our optical encoder enables ∼10 kHz rate operation of a hybrid ANN with a power of only 23 mW. However, in that regime, the overall classification accuracy is lower than what is achievable with higher power and latency. Our results indicate that optics can be advantageous over digital ANN in applications, where the overall performance of the ANN can be relaxed to prioritize lower power and latency. 

Foveated imaging provides a better tradeoff between situational awareness (field of view) and resolution, and is critical in long wavelength infrared regimes because of the size, weight, power, and cost of thermal sensors. We demonstrate computational foveated imaging by exploiting the ability of a meta-optical frontend to discriminate between different polarization states and a computational backend to reconstruct the captured image/video. The frontend is a three-element optic: the first element, which we call the “foveal” element, is a metalens that focuses s-polarized light at a distance off₁without affecting the p-polarized light; the second element, which we call the “perifovea” element, is another metalens that focuses p-polarized light at a distance off₂without affecting thes-polarized light. The third element is a freely rotating polarizer that dynamically changes the mixing ratios between the two polarization states. Both the foveal element (focal length=150mm; diameter=75mm) and the perifoveal element (focal length=25mm; diameter=25mm) were fabricated as polarization-sensitive, all-silicon, meta surfaces resulting in a large-aperture, 1:6 foveal expansion, thermal imaging capability. A computational backend then utilizes a deep image prior to separate the resultant multiplexed image or video into a foveated image consisting of a high resolution center and a lower-resolution large field of view context. We build a prototype system and demonstrate 12 frames per second real-time, thermal, foveated image and video capture.. 

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. 

A broad range of imaging and sensing technologies in the infrared require large field-of-view (FoV) operation. To achieve this, traditional refractive systems often employ multiple elements to compensate for aberrations, which leads to excess size, weight, and cost. For many applications, including night vision eye-wear, air-borne surveillance, and autonomous navigation for unmanned aerial vehicles, size and weight are highly constrained. Sub-wavelength diffractive optics, also known as meta-optics, can dramatically reduce the size, weight, and cost of these imaging systems, as meta-optics are significantly thinner and lighter than traditional refractive lenses. Here, we demonstrate 80° FoV thermal imaging in the long-wavelength infrared regime (8–12 µm) using an all-silicon meta-optic with an entrance aperture and lens focal length of 1 cm. 

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 Nano-optic imagers that modulate light at sub-wavelength scales could enable new applications in diverse domains ranging from robotics to medicine. Although metasurface optics offer a path to such ultra-small imagers, existing methods have achieved image quality far worse than bulky refractive alternatives, fundamentally limited by aberrations at large apertures and low f-numbers. In this work, we close this performance gap by introducing a neural nano-optics imager. We devise a fully differentiable learning framework that learns a metasurface physical structure in conjunction with a neural feature-based image reconstruction algorithm. Experimentally validating the proposed method, we achieve an order of magnitude lower reconstruction error than existing approaches. As such, we present a high-quality, nano-optic imager that combines the widest field-of-view for full-color metasurface operation while simultaneously achieving the largest demonstrated aperture of 0.5 mm at an f-number of 2.