arXiv subjects

Shane Colburn

Publications and source records attributed to Shane Colburn.

At least 19 recordsLinked to original sources

arXiv2026

Intrinsic Limitations of Single Layer Polychromatic Metalens for Virtual Reality Visors

Virtual and augmented reality (VR/AR) visors require compact and lightweight optics. Metalenses have been widely proposed as ultrathin replacements for bulky refractive eyepieces, with performance typically assessed using point spread function (PSF) and modulation transfer function (MTF) measurements. Here, we design, fabricate, and characterize a single-layer silicon nitride metalens optimized for the three emission peaks of an RGB OLED display, and benchmark it against refractive and Fresnel eyepieces. Under coherent illumination, the metalens exhibits a tightly confined PSF and strong mid-to-high spatial-frequency MTF, suggesting excellent optical performance. However, when evaluated in a realistic system-level VR testbed incorporating incoherent OLED illumination, a dynamic-pupil eye model, and near-eye-relevant focal lengths, the same device exhibits pronounced ghosting and background haze. We show that these artifacts arise from the intrinsic multifocal nature of polychromatic diffractive focusing and demonstrate that common mitigation strategies such as narrowband filtering and long-focal-length relay optics merely mask, rather than resolve, the issue. Our results establish that meta-optics for AR/VR must be evaluated under realistic system-level conditions to reveal their true imaging performance.

physics.optics↗

arXiv2025

Advantages of Broadband Metalenses for Generalizable Image Classification

Optical neural networks (ONNs) are gaining increasing attention to accelerate machine learning tasks. In particular, static meta-optical encoders designed for task-specific pre-processing have demonstrated orders of magnitude smaller energy consumption over purely digital counterparts, albeit at the cost of a slight degradation in classification accuracy. However, a lack of generalizability poses serious challenges for wide deployment of static meta-optical front-ends. Here, we investigate the utility of a single-layer metalens as a meta-optical encoder in ONNs for generalizable image classification. Specifically, we show that a visible-spectrum broadband metalens can achieve image classification accuracy comparable to high-end, sensor-limited optics and consistently outperforms the corresponding hyperboloid baseline across a wide range of sensor pixel sizes and digital backends. We further design an end-to-end optimized single-aperture metasurface for ImageNet classification and observe that the optimization tends to balance the modulation transfer function (MTF) across wavelengths within the sensor-detectable passband. Together, these observations suggest that the preservation of spatial-frequency information is an important factor influencing the performance of ONNs. Our results provide physical insight into the process of task-driven optical optimization and offer practical guidance for the design of high-performance ONNs and meta-optical encoders for generalizable computer vision tasks.

physics.optics↗

arXiv2025

Meta-optical Miniscope for Multifunctional Imaging

Miniaturized microscopes (miniscopes) have opened a new frontier in animal behavior studies, enabling real-time imaging of neuron activity while leaving animals largely unconstrained. Canonical designs typically use Gradient-Index (GRIN) lenses or refractive lenses as the objective module for excitation and fluorescence collection, but GRIN lenses suffer from aberrations and refractive lenses are bulky and complex. Meta-optics, composed of subwavelength diffractive elements, offer a promising alternative by combining multiple functionalities with significantly reduced footprint and weight. Here, we present meta-optical miniscopes that integrate functionalities including large field of view (FOV), extended depth of focus (EDOF), and depth sensitivity. These meta-optics replace the traditional refractive lens assembly, reducing the total track length of the objective module from 6.7 mm to 2.5 mm while enhancing imaging performance. Our results demonstrate that meta-optical miniscopes can expand the miniscope toolbox and facilitate the development of more compact and multifunctional imaging systems.

physics.optics↗

arXiv2024

Computed tomography using meta-optics

Computer vision tasks require processing large amounts of data to perform image classification, segmentation, and feature extraction. Optical preprocessors can potentially reduce the number of floating point operations required by computer vision tasks, enabling low-power and low-latency operation. However, existing optical preprocessors are mostly learned and hence strongly depend on the training data, and thus lack universal applicability. In this paper, we present a metaoptic imager, which implements the Radon transform obviating the need for training the optics. High quality image reconstruction with a large compression ratio of 0.6% is presented through the use of the Simultaneous Algebraic Reconstruction Technique. Image classification with 90% accuracy is presented on an experimentally measured Radon dataset through neural network trained on digitally transformed images.

cs.CV↗

arXiv2024

Compressed Meta-Optical Encoder for Image Classification

Optical and hybrid convolutional neural networks (CNNs) recently have become of increasing interest to achieve low-latency, low-power image classification and computer vision tasks. However, implementing optical nonlinearity is challenging, and omitting the nonlinear layers in a standard CNN comes at a significant reduction in accuracy. In this work, we use knowledge distillation to compress modified AlexNet to a single linear convolutional layer and an electronic backend (two fully connected layers). We obtain comparable performance to a purely electronic CNN with five convolutional layers and three fully connected layers. We implement the convolution optically via engineering the point spread function of an inverse-designed meta-optic. Using this hybrid approach, we estimate a reduction in multiply-accumulate operations from 17M in a conventional electronic modified AlexNet to only 86K in the hybrid compressed network enabled by the optical frontend. This constitutes over two orders of magnitude reduction in latency and power consumption. Furthermore, we experimentally demonstrate that the classification accuracy of the system exceeds 93% on the MNIST dataset.

cs.CV↗

arXiv2024

Beating bandwidth limits for large aperture broadband nano-optics

Flat optics have been proposed as an attractive approach for the implementation of new imaging and sensing modalities to replace and augment refractive optics. However, chromatic aberrations impose fundamental limitations on diffractive flat optics. As such, true broadband high-quality imaging has thus far been out of reach for low f-number, large aperture, flat optics. In this work, we overcome these intrinsic fundamental limitations, achieving broadband imaging in the visible wavelength range with a flat meta-optic, co-designed with computational reconstruction. We derive the necessary conditions for a broadband, 1 cm aperture, f/2 flat optic, with a diagonal field of view of 30{\deg} and an average system MTF contrast of 30% or larger for a spatial frequency of 100 lp/mm in the visible band (> 50 % for 70 lp/mm and below). Finally, we use a coaxial, dual-aperture system to train the broadband imaging meta-optic with a learned reconstruction method operating on pair-wise captured imaging data. Fundamentally, our work challenges the entrenched belief of the inability of capturing high-quality, full-color images using a single large aperture meta-optic.

physics.optics↗

arXiv2022

Real Time Full-Color Imaging in a Meta-Optical Fiber Endoscope

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.

physics.optics↗

arXiv2021

Millimeter-scale focal length tuning with MEMS-integrated meta-optics employing high-throughput fabrication

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.

physics.optics↗

arXiv2021

Fast Extended Depth of Focus Meta-Optics for Varifocal Functionality

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 pitch of meta-optics enables engineering very steep phase gradients, and thus meta-optics can achieve both a large physical aperture and 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 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 ~9.84{\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.

physics.optics↗

arXiv2021

Inverse Designed Extended Depth of Focus Meta-Optics for Broadband Imaging in the Visible

We report an inverse-designed, high numerical aperture (~0.44), extended depth of focus (EDOF) meta-optic, which exhibit 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 ~290nm optical bandwidth, which leads to significantly improved image quality, as quantified by structural similarity metrics.

physics.optics↗

arXiv2021

Knowledge Distillation Circumvents Nonlinearity for Optical Convolutional Neural Networks

In recent years, Convolutional Neural Networks (CNNs) have enabled ubiquitous image processing applications. As such, CNNs require fast runtime (forward propagation) to process high-resolution visual streams in real time. This is still a challenging task even with state-of-the-art graphics and tensor processing units. The bottleneck in computational efficiency primarily occurs in the convolutional layers. Performing operations in the Fourier domain is a promising way to accelerate forward propagation since it transforms convolutions into elementwise multiplications, which are considerably faster to compute for large kernels. Furthermore, such computation could be implemented using an optical 4f system with orders of magnitude faster operation. However, a major challenge in using this spectral approach, as well as in an optical implementation of CNNs, is the inclusion of a nonlinearity between each convolutional layer, without which CNN performance drops dramatically. Here, we propose a Spectral CNN Linear Counterpart (SCLC) network architecture and develop a Knowledge Distillation (KD) approach to circumvent the need for a nonlinearity and successfully train such networks. While the KD approach is known in machine learning as an effective process for network pruning, we adapt the approach to transfer the knowledge from a nonlinear network (teacher) to a linear counterpart (student). We show that the KD approach can achieve performance that easily surpasses the standard linear version of a CNN and could approach the performance of the nonlinear network. Our simulations show that the possibility of increasing the resolution of the input image allows our proposed 4f optical linear network to perform more efficiently than a nonlinear network with the same accuracy on two fundamental image processing tasks: (i) object classification and (ii) semantic segmentation.

cs.CV↗

arXiv2021

Neural Nano-Optics for High-quality Thin Lens Imaging

Nano-optic imagers that modulate light at sub-wavelength scales could unlock unprecedented 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 presenting the first neural nano-optics. We devise a fully differentiable learning method that learns a metasurface physical structure in conjunction with a novel, neural feature-based image reconstruction algorithm. Experimentally validating the proposed method, we achieve an order of magnitude lower reconstruction error. As such, we present the first high-quality, nano-optic imager that combines the widest field of view for full-color metasurface operation while simultaneously achieving the largest demonstrated 0.5 mm, f/2 aperture.

physics.optics↗

arXiv2021

2D beam shaping via 1D spatial light modulation

Many emerging reconfigurable optical systems are limited by routing complexity when producing dynamic, two-dimensional (2D) electric fields. Using a gradient-based inverse designed, static phase-mask doublet, we propose an optical system to produce 2D intensity wavefronts using a one-dimensional (1D) intensity Spatial Light Modulator (SLM). We show the capability of mapping each point in a 49 element 1D array to a distinct 7x7 2D spatial distribution. Our proposed method will significantly relax the routing complexity of 2D sub-wavelength SLMs, possibly enabling next-generation SLMs to leverage novel pixel architectures and new materials.

physics.optics↗

arXiv2020

Inverse design and flexible parameterization of meta-optics using algorithmic differentiation

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.

physics.optics↗

arXiv2020

Design of achromatic augmented reality visors based on composite metasurfaces

A compact near-eye visor (NEV) system that can guide light from a display to the eye could transform augmented reality (AR) technology. Unfortunately, existing implementations of such an NEV either suffer from small field of view or chromatic aberrations. See-through quality and bulkiness further make the overall performance of the visors unsuitable for a seamless user experience. Metasurfaces are an emerging class of nanophotonic elements that can dramatically reduce the size of optical elements while enhancing functionality. In this paper, we present a design of composite metasurfaces for an ultra-compact NEV. We simulate the performance of a proof-of-principle visor corrected for chromatic aberrations while providing a large display field of view (>77{\deg} both horizontally and vertically), good see-through quality (>70% transmission and less than a wavelength root mean-square (RMS) wavefront error over the whole visible wavelength range), as needed for an immersive AR experience.

physics.optics↗

arXiv2020

Design and Analysis of Extended Depth of Focus Metalenses for Achromatic Computational Imaging

Metasurface optics have demonstrated vast potential for implementing traditional optical components in an ultra-compact and lightweight form factor. Metasurface lenses, also called metalenses, however, suffer from severe chromatic aberrations, posing serious limitations on their practical use. Existing approaches for circumventing such aberrations via dispersion engineering are limited to small apertures and often entails multiple scatterers per unit cell with small feature sizes. Here, we present an alternative technique to mitigate chromatic aberration and demonstrate high-quality, full-color imaging using extended depth of focus (EDOF) metalenses and computational reconstruction. Previous EDOF metalenses relied on cubic phase masks that induced asymmetric artifacts in images, whereas here we demonstrate the use of symmetric phase masks that can improve subsequent image quality, including logarithmic-aspherical, and shifted axicon masks. Our work will inspire further development in achromatic metalenses beyond dispersion engineering and open new research avenues on hybrid optical-digital metasurface systems.

physics.optics↗

arXiv2020

MEMS-actuated Metasurface Alvarez Lens

Miniature lenses with tunable focus are essential components for many modern applications involving compact optical systems. While several tunable lenses have been reported with various tuning mechanisms, they often still face challenges in power consumption, tuning speed, fabrication cost, or production scalability. In this work, we have adapted the mechanism of an Alvarez lens - a varifocal composite lens in which lateral shifts of two optical elements with cubic phase surfaces give rise to a change in optical power - to construct a miniature, MEMS-actuated metasurface Alvarez lens. The implementation based on electrostatic microelectromechanical systems (MEMS) generates fast and controllable actuation with low power consumption. The utilization of metasurfaces, ultrathin and subwavelength-patterned diffractive optics, as the optical elements greatly reduces the device volume compared to systems using conventional freeform lenses. The entire MEMS Alvarez metalens is fully compatible with modern semiconductor fabrication technologies, granting it the potential to be mass-produced at a low unit cost. In the reported prototype to operate at 1550 nm wavelength, a total uniaxial displacement of 6.3 um is achieved in the Alvarez metalens with direct-current (DC) voltage application up to 20 V, modulating the focal position within a total tuning range of 68 um, producing more than an order of magnitude change in focal length and 1460 diopters change in optical power. The MEMS Alvarez metalens has a robust design that can potentially generate a much larger tuning range without substantially increasing device volume or energy consumption, making it desirable for a wide range of imaging and display applications.

physics.optics↗

arXiv2019

Single-shot three-dimensional imaging with a metasurface depth camera

Depth imaging is vital for many emerging technologies with applications in augmented reality, robotics, gesture detection, and facial recognition. These applications, however, demand compact and low-power systems beyond the capabilities of state-of-the-art depth cameras. Here, we leverage ultrathin dielectric metasurfaces to demonstrate a solution that, with a single surface, replicates the functionality of a high-performance depth camera typically comprising a spatial light modulator, polarizer, and three lenses. Using cylindrical nano-scatterers that can arbitrarily modify the phase of an incident wavefront, our metasurface passively encodes two complementary optical responses to depth information in a scene with a single camera snapshot. By decoding the captured data in software, our system produces a fully reconstructed image and transverse depth map of three-dimensional scenes with a fractional ranging error of 1.7%. We demonstrate the first visible wavelength and polarization-insensitive metasurface depth camera, representing a significant form factor reduction for such systems.

physics.optics↗