Search

An optical vortex beam has attracted significant attention across diverse applications, including optical manipulation, phase-contrast microscopy, optical communication, and quantum photonics. To utilize vortex generators for integrated photonics, researchers have developed ultra-compact vortex generators using fork gratings, metasurfaces, and integrated microcombs. However, those devices depend on costly, time-consuming nanofabrication and are constrained by the low signal-to-noise ratio due to the fabrication error. As an alternative maneuver, spin-orbit coupling has emerged as a method to obtain the vortex beam by converting spin angular momentum (SAM) without nanostructures. Here, we demonstrate the creation of an optical vortex beam using van der Waals (vdW) materials. The significantly high birefringence of vdW materials allows generations of optical vortex beams with high efficiency in a sub-wavelength thickness. In this work, we utilize an 8-um-thick hexagonal boron nitride (hBN) crystal for the creation of optical vortices carrying topological charges of +2 and -2. We also present the generation of an optical vortex beam in a 320-nm-thick MoS2 crystal with a conversion efficiency of 0.09. This study paves the way for fabrication-less and ultra-compact optical vortex generators, which can be applied for integrated photonics and large-scale vortex generator arrays., Comment: 15 pages, 7 figures

Facial wrinkle detection plays a crucial role in cosmetic dermatology. Precise manual segmentation of facial wrinkles is challenging and time-consuming, with inherent subjectivity leading to inconsistent results among graders. To address this issue, we propose two solutions. First, we build and release the first public facial wrinkle dataset, 'FFHQ-Wrinkle', an extension of the NVIDIA FFHQ dataset. It includes 1,000 images with human labels and 50,000 images with automatically generated weak labels. This dataset could serve as a foundation for the research community to develop advanced wrinkle detection algorithms. Second, we introduce a simple training strategy utilizing texture maps, applicable to various segmentation models, to detect wrinkles across the face. Our two-stage training strategy first pretrain models on a large dataset with weak labels (N=50k), or masked texture maps generated through computer vision techniques, without human intervention. We then finetune the models using human-labeled data (N=1k), which consists of manually labeled wrinkle masks. The network takes as input a combination of RGB and masked texture map of the image, comprising four channels, in finetuning. We effectively combine labels from multiple annotators to minimize subjectivity in manual labeling. Our strategies demonstrate improved segmentation performance in facial wrinkle segmentation both quantitatively and visually compared to existing pretraining methods. The dataset is available at https://github.com/labhai/ffhq-wrinkle-dataset.

Designing photonic structures requires electromagnetic simulations, which often require high computational costs. Researchers have developed surrogate solvers for predicting electric fields to alleviate the computational issues. However, existing surrogate solvers are limited to performing inference at fixed simulation conditions and require retraining for different conditions. To address this, we propose Wave Interpolation Neural Operator (WINO), a novel surrogate solver enabling simulation condition interpolation across a continuous spectrum of broadband wavelengths. WINO introduces the Fourier Group Convolution Shuffling operator and a new conditioning method to efficiently predict electric fields from both trained and untrained wavelength data, achieving significant improvements in parameter efficiency and spectral interpolation performance. Our model demonstrates approximately 100 times faster performance than traditional finite-difference frequency-domain simulations. Moreover, compared to the state-of-the-art model, we achieve a 74% reduction in parameters and 80.5% improvements in prediction accuracy for untrained wavelengths, and 13.2% improvements for trained wavelengths., Comment: 9 pages, 5 figures, 4 tables / Appendix: 4 pages, 4 figures, 3 tables

Alzheimer's Disease (AD) is the most common form of dementia, characterised by cognitive decline and biomarkers such as tau-proteins. Tau-positron emission tomography (tau-PET), which employs a radiotracer to selectively bind, detect, and visualise tau protein aggregates within the brain, is valuable for early AD diagnosis but is less accessible due to high costs, limited availability, and its invasive nature. Image synthesis with neural networks enables the generation of tau-PET images from more accessible T1-weighted magnetic resonance imaging (MRI) images. To ensure high-quality image synthesis, we propose a cyclic 2.5D perceptual loss combined with mean squared error and structural similarity index measure (SSIM) losses. The cyclic 2.5D perceptual loss sequentially calculates the axial 2D average perceptual loss for a specified number of epochs, followed by the coronal and sagittal planes for the same number of epochs. This sequence is cyclically performed, with intervals reducing as the cycles repeat. We conduct supervised synthesis of tau-PET images from T1w MRI images using 516 paired T1w MRI and tau-PET 3D images from the ADNI database. For the collected data, we perform preprocessing, including intensity standardisation for tau-PET images from each manufacturer. The proposed loss, applied to generative 3D U-Net and its variants, outperformed those with 2.5D and 3D perceptual losses in SSIM and peak signal-to-noise ratio (PSNR). In addition, including the cyclic 2.5D perceptual loss to the original losses of GAN-based image synthesis models such as CycleGAN and Pix2Pix improves SSIM and PSNR by at least 2% and 3%. Furthermore, by-manufacturer PET standardisation helps the models in synthesising high-quality images than min-max PET normalisation., Comment: 24 pages, 5 figures

Increasing attention is being paid to chiral metasurfaces due to their ability to selectively manipulate right-hand circularly polarized light or left-hand circularly polarized light. The thin nature of metasurfaces, however, poses a challenge in creating a device with effective phase modulation. Plasmonic chiral metasurfaces have attempted to address this issue by increasing light-matter interaction, but they suffer from metallic loss. Dielectric metasurfaces made from high index materials enable phase modulation while being thin. Very few materials, however, have high refractive index and low loss at visible wavelengths. Recently, some 2D materials have been shown to exhibit high refractive index and low loss in the visible wavelengths, positioning them as promising platform for meta-optics. This study introduces and details a planar chiral metasurface with geometric phase composed of WS2 meta-units. By employing adjoint optimization techniques, we achieved broadband circular dichroism., Comment: 9 pages, 5 figures

Recent advances in metasurface lenses (metalenses) have shown great potential for opening a new era in compact imaging, photography, light detection and ranging (LiDAR), and virtual reality/augmented reality (VR/AR) applications. However, the fundamental trade-off between broadband focusing efficiency and operating bandwidth limits the performance of broadband metalenses, resulting in chromatic aberration, angular aberration, and a relatively low efficiency. In this study, a deep-learning-based image restoration framework is proposed to overcome these limitations and realize end-to-end metalens imaging, thereby achieving aberration-free full-color imaging for mass-produced metalenses with 10-mm diameter. Neural-network-assisted metalens imaging achieved a high resolution comparable to that of the ground truth image., Comment: 17 pages, 7 figures, 1 table

Innovative machine learning techniques have facilitated the inverse design of photonic structures for numerous practical applications. Nevertheless, within these approaches, the quantity of data and the initial data distribution are paramount for the discovery of highly efficient photonic devices. These devices often require simulated data ranging from thousands to several hundred thousand data points. This issue has consistently posed a major hurdle in machine learning-based photonic design problems. Therefore, we propose a novel data augmentation algorithm grounded in the adjoint method, capable of generating more than 300 times the amount of original data while enhancing device efficiency. The adjoint method forecasts changes in the figure of merit (FoM) resulting from structural perturbations, requiring only two full-wave Maxwell simulations for this prediction. By leveraging the adjoint gradient values, we can augment and label several thousand new data points without any additional computations. Furthermore, the augmented data generated by the proposed algorithm displays significantly improved FoMs owing to the precise FoM change predictions enabled by the adjoint gradients. We apply this algorithm to a multi-layered metalens design problem and demonstrate that it consequently exhibits a 343-fold increase in data generation efficiency. After incorporating the proposed algorithm into a generative adversarial network (GAN), the optimized metalens exhibits a maximum focusing efficiency of 92.93%, comparable to the theoretical upper bound (93.80%)., Comment: 12 pages, 6 figures

Optical vortices (OVs) have rapidly varying spatial phase and optical energy that circulates around points or lines of zero optical intensity. Manipulation of OV offers innovative approaches for various fields, such as optical sensing, communication, and imaging. In this work, we demonstrate the correlation between OVs and absorption enhancement in two types of structures. First, we introduce a simple planar one-dimensional (1D) structure that manipulates OVs using two coherent light sources. The structure shows a maximum of 6.05-fold absorption gap depending on the presence of OVs. Even a slight difference in the incidence angle can influence the generation/annihilation of OVs, which implies the high sensitivity of angular light detection. Second, we apply inverse design to optimize two-dimensional (2D) perfect ultrathin absorbers. The optimized free-form structure achieves 99.90% absorptance, and the fabricable grating structure achieves 97.85% at 775 nm wavelength. To evaluate OV fields and their contribution to achieving absorption enhancement, we introduce a new parameter, OV circularity. The optimized structures generate numerous OVs with a maximum circularity of 95.37% (free-form) and 96.14% (grating), superior to our 1D structure. Our study reveals the role of high-circularity localized OVs in optimizing nano-structured absorbers and devices for optical sensing, optical communication, and many other applications., Comment: 15 pages, 11 figures

Metasurface-based color routers are emerging as next-generation optical components for image sensors, replacing classical color filters and microlens arrays. In this work, we report how the design parameters such as the device dimensions and refractive indices of the dielectrics affect the optical efficiency of the color routers. Also, we report how the design grid resolution parameters affect the optical efficiency and discover that the fabrication of a color router is possible even in legacy fabrication facilities with low structure resolutions.

We demonstrate an axisymmetric inverse-designed metalens to improve the performance of zone-plate-array lithography (ZPAL), one of the maskless lithography approaches, that offer a new paradigm for nanoscale research and industry. First, we derive a computational upper bound for a unit-cell-based axisymmetric metalens. Then, we demonstrate a fabrication-compatible inverse-designed metalens with 85.50\% transmission normalized focusing efficiency at 0.6 numerical aperture at 405nm wavelength; a higher efficiency than a theoretical gradient index lens design (79.98\%). We also demonstrate experimental validation for our axisymmetric inverse-designed metalens via electron beam lithography. Metalens-based maskless lithography may open a new way of achieving low-cost, large-area nanofabrication.

Tunable metasurfaces have demonstrated the potential for dramatically enhanced functionality for applications including sensing, ranging and imaging. Liquid crystals (LCs) have fast switching speeds, low cost, and mature technological development, offering a versatile platform for electrical tunability. However, to date, electrically tunable metasurfaces are typically designed at a single operational state using physical intuition, without controlling alternate states and thus leading to limited switching efficiencies ($<30\%$) and small angular steering ($<$25$^\circ$). Here, we use large-scale computational 'inverse design' to discover high-performance designs through adjoint-based local-optimization design iterations within a global-optimization search. We study and explain the physics of these devices, which heavily rely on sophisticated resonator design to fully utilize the very small permittivity change incurred by switching the liquid-crystal voltage. The optimal devices show tunable steering angles ranging from 12$^\circ$ to 144$^\circ$ and switching efficiencies above 80%, exhibiting 6X angular improvements and 6X efficiency improvements compared to the current state-of-the-art.

We derive upper bounds to free-space concentration of electromagnetic waves, mapping out the limits to maximum intensity for any spot size and optical beam-shaping device. For sub-diffraction-limited optical beams, our bounds suggest the possibility for orders-of-magnitude intensity enhancements compared to existing demonstrations, and we use inverse design to discover metasurfaces operating near these new limits. We also demonstrate that our bounds may surprisingly describe maximum concentration defined by a wide variety of metrics. Our bounds require no assumptions about symmetry, scalar waves, or weak scattering, instead relying primarily on the transformation of a quadratic program via orthogonal-projection methods. The bounds and inverse-designed structures presented here can be useful for applications from imaging to 3D printing., Comment: title capitalization corrected

We use inverse design to discover metalens structures that exhibit broadband, achromatic focusing across low, moderate, and high numerical apertures. We show that standard unit-cell approaches cannot achieve high-efficiency high-NA focusing, even at a single frequency, due to the incompleteness of the unit-cell basis, and we provide computational upper bounds on their maximum efficiencies. At low NA, our devices exhibit the highest theoretical efficiencies to date. At high NA -- of 0.9 with translation-invariant films and of 0.99 with "freeform" structures -- our designs are the first to exhibit achromatic high-NA focusing.