Far-field Imaging Research Articles

High-resolution true-color imaging based on wavelength-multiplexed far-field spatial-frequency shift imaging

As an innovative computational imaging technique proposed in recent years, spatial-frequency shift (SFS) technique shifts the high-frequency components into the passband of the system, thereby successfully overcoming the intrinsic trade-off between resolution and field-of-view (FOV). However, in practical far-field SFS scenarios, the extended propagation distance may lead to the degradation of the coherence and introduce ambient noise. Besides, color information is crucial for revealing subtle details, whereas most of the extant far-field SFS research concentrates on monochromatic recovery. Here, we report a high-resolution (HR) true-color imaging, termed wavelength-multiplexed far-field spatial-frequency shift (WMSFS). We demonstrate that WMSFS yields an enhancement in Signal-to-Noise Ratio (SNR) under high-level noise contamination. The WMSFS approach is also validated experimentally with the USAF 1951 resolution chart and butterfly wings sample. 2.83-fold spatial resolution improvement with accurate representations of color was achieved for the sample over a 1 m distance. Compared with conventional single-wavelength far-field SFS, WMSFS is capable of achieving true-color restoration of objects without compromising the high-resolution capabilities of the system. Further hyperspectral and high-resolution remote imaging might be obtained simultaneously based on this method.

A-SiC heteromorphic immersion nanocavities enabling wide-field real-time single-molecule detection

Real-time single-molecule detection via fluorescence exhibits advantages of non-contact and specificity, especially in illustrating the dynamic heterogeneity of living substances. However, wide-field view and signal-to-noise ratio (SNR) are always contradictory in real-time single-molecule detection with fluorescence labels, owing to the limitation of the omnidirectional radiation characteristics of fluorophores. Herein, we propose a nano optical sensing device based on a-SiC heteromorphic immersion nanocavities (aHINCs), enabling wide-field real-time single-molecule imaging without sacrificing SNR. The characteristics of architectural aHINCs for far-field imaging are investigated, and the designed sensing device help adjust the emission direction of fluorescence in gold hot spots of nanocavities, allowing the fluorescence divergence angle to be adjusted to ±10°. The experimental results show the SNR reached 22, markedly strengthening the fluorescence collection efficiency. The simultaneous observation of nanocavity sites, within the same field of view of the chip, is 488 times the number of sites observed with classic detection method. Furthermore, the wide-field real-time detection of the single molecule–specific binding process of the oligo DNA complementary chain is successfully realized. The nano optical sensing device based on the aHINC shows potential for parallel real-time single-molecule detection applications.

Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer Learning

The resolution of a telescope is closely related to its aperture size; however, the aperture of a single primary mirror telescope cannot be indefinitely enlarged due to design and manufacturing constraints. Segmented mirror technology can achieve the same resolution as a single primary mirror of equivalent aperture, provided that the segments are co-phased correctly. This paper proposes a method for high-precision detection of piston errors in segmented mirror telescope systems, based on far-field information and transfer learning. By training a ResNet-18 network model, this method can predict piston errors with high precision within 10 ms of a single-frame far-field diffraction image. Simulation results demonstrate that the method is robust to tip-tilt errors, wavefront aberrations, and noise. This approach is simple, fast, highly accurate in detection, and resistant to noise, providing a new solution for piston error detection in segmented mirror systems.

Quasi-visualizable detection of deep sub-wavelength defects in patterned wafers by breaking the optical form birefringence

Abstract In integrated circuit (IC) manufacturing, fast, nondestructive, and precise detection of defects in patterned wafers, realized by bright-field microscopy, is one of the critical factors for ensuring the final performance and yields of chips. With the critical dimensions of IC nanostructures continuing to shrink, directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier, the weak scattering effect, and the faint correlation between the scattering cross-section and the defect morphology. Herein, we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure, which can identify and classify various defects without requiring optical super-resolution. The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes, such as the orthogonally polarized plane waves, then combined with the high-order difference of far-field images. We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy. On this basis, an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed, stabilizing the classification accuracy of λ/16-sized defects with highly similar features at more than 90%. The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input, which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results. This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks, which thus endows current online inspection equipment with more missions in advanced IC manufacturing.

Further study of the Singer array in near-field coded aperture imaging for extended sources

Although coded aperture imaging (CAI) has been successfully applied in far-field imaging, its near-field applications suffer from significant declines in image quality caused by near-field artifacts, whose characteristics are related to basic coded patterns. In this study, near-field extended-source CAI performances based on the Singer array (SA) are discussed, where a 29 × 33 SA mask is selected in comparison with a single pinhole and a 33 × 33 mask based on the modified uniformly redundant array. Simplified near-field corrections are introduced in the reconstruction process of SA imaging, whose effectiveness in extended-source imaging is discussed through a series of simulations, and high-resolution near-field imaging experiments of sealed sources are conducted for further validation. Moreover, its potential application of nondestructive testing based on Compton scattering tomography is discussed. The simulation and experiment results show potential in SA-based CAI to achieve fast, real-time, and even dynamic high-resolution near-field applications for complicated objects.

A Comprehensive Comparison of Far-Field and Near-Field Imaging Radiometry in Synthetic Aperture Interferometry

A Comprehensive Comparison of Far-Field and Near-Field Imaging Radiometry in Synthetic Aperture Interferometry

Non-interleaved quad-channel metasurfaces for simultaneous nanoprinting and holography enabled by phase and bidirectional intensity modulation

Metasurfaces, with their unparalleled ability to manipulate light-field characteristics, have brought forth numerous advantages in ultra-compact and high-resolution image displays, particularly in multifunctional metadevices for simultaneous nanoprinting and holography. However, current efforts are hindered by challenges such as increased fabrication complexity, low efficiencies, or being confined to classic two/three-channel configurations, limiting their further applications. In this study, we propose a single-cell non-interleaved metasurface platform capable of simultaneously and independently generating two holographic images in the far field and two nanoprinting images in the near field. Through a spin-decoupled phase modulation, the metasurface can produce two far-field holographic images under orthogonal circularly polarized incidences. By fully unlocking the Malus's law-assisted bidirectional intensity modulation, the metasurface enables the reconstruction of two near-field nanoprinting images with two distinct orthogonal polarization optical setups as decoding keys. Moreover, the quad-channel metasurface we proposed exhibits broadband response and on-off binary switching performance enabled by the phase transition of Ge2Sb2Se4Te1. We envision that the proposed design paradigm opens up new possibilities for expanding the functionality and enhancing the capacity of metasurfaces without burdening their design, thereby paving the way for compact multifunctional optical devices in image display, information encoding, anti-counterfeiting, and other related fields.

Untrained single scanning phase retrieval neural network for surface deformation measurement of large-aperture antennas

Research on data-driven Fourier phase retrieval neural networks is well-established. However, in many practical applications, such as phase retrieval-based antenna surface deformation measurements in astronomy, obtaining a sufficient number of ground truth images for training is unlikely. In this paper, we propose a purely physics-driven neural network model for retrieving the aperture-field phase of large antennas. This is accomplished through the integration of the physical model describing the interaction between the aperture-field and the far-field with the neural network. Our simulations and experiments demonstrate accurate reconstruction of the aperture-field phase using only a single far-field intensity image. Furthermore, we present a solution tailored for scenarios with low signal-to-noise ratio.

Bridge the gap between simulated and real-world data in optical fiber mode decomposition for accuracy improvement: A deep learning-based co-learning framework with visual similarity-based matching

In the area of optical fiber mode decomposition (MD), data-driven deep-learning (DL) algorithms have achieved remarkable strides with unlimited theoretically synthetic data, but the differences between theoretical and real-world data in the DL context are rarely investigated. The two differences, namely the information bottleneck and the domain discrepancy, between the simulation and realistic domain remain significant obstacles that limit the practical deployment of DL-based MD approaches in real-world optical systems. The paper aims to mitigate their influence on the accuracy limitation and the accuracy degradation of DL-based MD. First, we present a DL-based co-learning framework for MD for the first time. With the co-learning process, the knowledge of the teacher model learned from rich input information sources (near-field, far-field, and phase-distribution optical images) is transferred to the student model with the only near-field input source, which breaks the information bottleneck by allowing the student model to learn more knowledge in a simulation context. Furthermore, to settle the domain discrepancy between ideal data and practical observation, we propose a visual similarity-based matching to assign real values of modal coefficients to the practical optical images whose ground truth values cannot be easily acquired, which enables the transfer learning from the synthetic data to the real-world data for accuracy enhancement. Simulated and experimental results show that the proposed co-learning framework with visual similarity-based matching has realized high precision and robustness in MD simulated and practical tasks. By bridging the gap between theoretical and practical data, our study offers novel perspectives on DL-based MD practice.

Topography reconstruction of high aspect ratio silicon trench array via near-infrared coherence scanning interferometry.

Topography measurement of high aspect ratio trench array using coherence scanning interferometry presents significant challenges because the numerical aperture of detection light is constrained by the trenches. Altering the detection light to penetrate the sample like near-infrared light for silicon could overcome this obstacle, but the trench array spreads the detection light. This study introduces a coherence scanning interferometry model based on three-dimensional point spread function and assuming sample is transparent to detection light, which is realized by integrating rigorous numerical electromagnetic field solution to quantify the modulation aberrations of detection light by transparent trench arrays, and theoretical angular spectrum diffraction utilized for far-field interference imaging. This model facilitates a thorough analysis of the aberrations introduced by trench arrays, encompassing comparisons between trench arrays and a single trench, as well as between the symmetric region of the array and the asymmetric region at the edge. Additionally, an investigation into the impact of unified compensation for low-order aberrations on the topography reconstruction is presented, and we find the sample-induced aberration compensation method utilizing a deformable mirror that we previously proposed for a single trench is still effective confronting trench array. Experimental measurements are performed on silicon trench arrays with the aspect ratio of up to 20:1 and the period of approximately 10 µm to validate the effectiveness of our model and measurement methods, thus providing valuable insights for enhancing high aspect ratio manufacturing.

Phase-matched third-harmonic generation in silicon nitride waveguides.

Third-harmonic generation (THG) in silicon nitride waveguides is an ideal source of coherent visible light, suited for ultrafast pulse characterization, telecom signal monitoring and self-referenced comb generation due to its relatively large nonlinear susceptibility and CMOS compatibility. We demonstrate third-harmonic generation in silicon nitride waveguides where a fundamental transverse mode at 1,596 nm is phase-matched to a TM02 mode at 532 nm, confirmed by the far-field image. We experimentally measure the waveguide width-dependent phase-matched wavelength with a peak-power-normalized conversion efficiency of 5.78 × 10-7 %/W2 over a 660-μm-long interaction length.

Full-Stokes polarization detection enabled by a terahertz all-dielectric metasurface

Metasurface has the ability to flexibly modulate the wavefront and detect the polarization states, thus receiving widespread research attention. The combination of polarization multiplexing techniques and focused beams with polarization information provides a new approach to compact polarization detection behavior. Here, an all-dielectric metasurface based on polarization multiplexing encoding technique is demonstrated and assigns the independent phase distributions to x- and y-polarized channels to separate different polarized components. Combining Stokes parameters and visualized polarization ellipses, realizing one-to-one mapping of the incident polarization states with far-field images. We obtain the Poincaré sphere and visualized polarization ellipses based on Stokes parameters to verify the polarization detection ability of the designed metasurface. The proposed scheme is expected to provide potential applications in fields such as full-Stokes polarization detection, high-resolution imaging, and terahertz communication.

Efficient Scaling Techniques for 2-D Sparse MIMO Array Far-Field Imaging

Efficient Scaling Techniques for 2-D Sparse MIMO Array Far-Field Imaging

Numerical study of transient absorption saturation in single-layer graphene for optical nanoscopy applications

Transient absorption, or pump–probe microscopy is an absorption-based technique that can explore samples ultrafast dynamic properties and provide fluorescence-free contrast mechanisms. When applied to graphene and its derivatives, this technique exploits the graphene transient response caused by the ultrafast interband transition as the imaging contrast mechanism. The saturation of this transition is fundamental to allow for super-resolution optical far-field imaging, following the reversible saturable optical fluorescence transitions (RESOLFT) concept, although not involving fluorescence. With this aim, we propose a model to numerically compute the temporal evolution under saturation conditions of the single-layer graphene molecular states, which are involved in the transient absorption. Exploiting an algorithm based on the fourth order Runge–Kutta (RK4) method, and the density matrix approach, we numerically demonstrate that the transient absorption signal of single-layer graphene varies linearly as a function of excitation intensity until it reaches saturation. We experimentally verify this model using a custom pump–probe super-resolution microscope. The results define the intensities necessary to achieve super-resolution in a pump–probe nanoscope while studying graphene-based materials and open the possibility of predicting such a saturation process in other light-matter interactions that undergo the same transition.

Deep learning empowers photothermal microscopy with super-resolution capabilities.

In the past two decades, photothermal microscopy (PTM) has achieved sensitivity at the level of a single particle or molecule and has found applications in the fields of material science and biology. PTM is a far-field imaging method; its resolution is restricted by the diffraction limits. In our previous work, the modulated difference PTM (MDPTM) was proposed to improve the lateral resolution, but its resolution improvement was seriously constrained by information loss and artifacts. In this Letter, a deep learning approach of the cycle generative adversarial network (Cycle GAN) is employed for further improving the resolution of PTM, called DMDPTM. The point spread functions (PSFs) of both PTM and MDPTM are optimized and act as the second generator of Cycle GAN. Besides, the relationship between the sample's volume and the photothermal signal is utilized during dataset construction. The images of both PTM and MDPTM are utilized as the inputs of the Cycle GAN to incorporate more information. In the simulation, DMDPTM quantitatively distinguishes a distance of 60 nm between two nanoparticles (each with a diameter of 60 nm), demonstrating a 4.4-fold resolution enhancement over the conventional PTM. Experimentally, the super-resolution capability of DMDPTM is verified by restored images of Au nanoparticles, achieving the resolution of 114 nm. Finally, the DMDPTM is successfully employed for the imaging of carbon nanotubes. Therefore, the DMDPTM will serve as a powerful tool to improve the lateral resolution of PTM.

Classification of laser beam profiles using machine learning at the ELI-NP high power laser system

The high power laser system at Extreme Light Infrastructure—Nuclear Physics has demonstrated 10 PW power shot capability. It can also deliver beams with powers of 1 PW and 100 TW in several different experimental areas that carry out dedicated sets of experiments. An array of diagnostics is deployed to characterize the laser beam spatial profiles and to monitor their evolution during the amplification stages. Some of the essential near-field and far-field profiles acquired with CCD cameras are monitored constantly on a large screen television for visual observation and for decision making concerning the control and tuning of the laser beams. Here, we present results on the beam profile classification obtained from datasets with over 14 600 near-field and far-field images acquired during two days of laser operation at 1 PW and 100 TW. We utilize supervised and unsupervised machine learning models based on trained neural networks and an autoencoder. These results constitute an early demonstration of machine learning being used as a tool in the laser system data classification.

Polarization-insensitive high-numerical-aperture metalens for wide-field super-resolution imaging.

The development of super-oscillatory lens (SOL) offers opportunities to realize far-field label-free super-resolution microscopy. Most microscopes based on a high numerical aperture (NA) SOL operate in the point-by-point scanning mode, resulting in a slow imaging speed. Here, we propose a high-NA metalens operating in the single-shot wide-field mode to achieve real-time super-resolution imaging. An optimization model based on the exhaustion algorithm and angular spectrum (AS) theory is developed for metalens design. We numerically demonstrate that the optimized metalens with an NA of 0.8 realizes the imaging resolution (imaging pixel size) about 0.85 times the Rayleigh criterion. The metalens can achieve super-resolution imaging of an object with over 200 pixels, which is one order of magnitude higher than the unoptimized metalens. Our method provides an avenue toward single-shot far-field label-free super-resolution imaging for applications such as real-time imaging of living cells and temporally moving particles.

Design and Analysis of the Dual-Band Far-Field Super-Resolution Metalens with Large Aperture.

The resolving power of metalens telescopes rely on their aperture size. Flat telescopes are advancing with the research on super-resolution confocal metalenses with large aperture. However, the aperture sizes of metalenses are usually bound within hundreds of micrometers due to computational and fabrication challenges, limiting their usage on practical optical devices like telescopes. In this work, we demonstrated a two-step designing method for the design of dual-band far-field super-resolution metalens with aperture sizes from the micro-scale to macro-scale. By utilizing two types of inserted unit cells, the phase profile of a dual-wavelength metalens with a small aperture of 100 μm was constructed. Through numerical simulation, the measured FWHM values of the focal spots of 5.81 μm and 6.81 μm at working wavelengths of 632.8 nm and 1265.6 nm were found to all be slightly smaller than the values of 0.61 λ/NA, demonstrating the super-resolution imaging of the designed metalens. By measuring the optical power ratio of the focal plane and the incident plane, the focusing efficiencies were 76% at 632.8 nm and 64% at 1265.6 nm. Based on the design method for small-aperture metalens, far-field imaging properties through the macro metalens with an aperture of 40 mm were simulated by using the Huygens-Fresnel principle. The simulation results demonstrate confocal far-field imaging behavior at the target wavelengths of 632.8 nm and 1265.6 nm, with a focal length of 200 mm. The design method for dual-band far-field super-resolution metalens with a large aperture opens a door towards the practical applications in the dual-band space telescope system.

Ensemble Learning Model for Damage Detection Using Deep Convolutional Networks

Damages to crops happen due to natural calamities, irregular fertilization, improper treatment, etc. To perform this estimation of this damage with high accuracy, both satellite & near-field images are needed. Satellite images assists in evaluation of damages due to natural calamities, while near-field images assist in evaluation of damage due to plant diseases. Separate models are designed for processing these images, which limits their correlative analysis; and thereby reduces overall accuracy of damage detection. To remove this drawback, this text proposes a deep convolutional network (DCN) design that integrates both near-field and far-field images in order to perform effective correlation. The model is trained for detection of areas which are infected by natural calamities, thereby assisting farm experts to undertake corrective measures based on specific area. Results of proposed model are compared with some of the recently developed state-of-the-art methods, and it is observed that the former model achieves 10% better accuracy, performance.

Angular Dispersion of Free-Electron-Light Coupling in an Optical Fiber-Integrated Metagrating.

Free electrons can couple to optical material excitations on nanometer-length and attosecond-time scales, opening-up unique opportunities for both the generation of radiation and the manipulation of the electron wave function. Here, we exploit the Smith-Purcell effect to experimentally study the coherent coupling of free electrons and light in a circular metallo-dielectric metagrating that is fabricated onto the input facet of a multimode optical fiber. Using hyperspectral angle-resolved (HSAR) far-field imaging inside a scanning electron microscope, we probe the angular dispersion of Smith-Purcell radiation (SPR) that is simultaneously generated in free space and inside the fiber by an electron beam that grazes the metagrating at a nanoscale distance. Furthermore, we analyze the spectral distribution of SPR that is emitted into guided optical modes and correlate it with the numerical aperture of the fiber. By varying the electron energy between 5 and 30 keV, we observe the emission of SPR from the ultraviolet to the near-infrared spectral range, and up to the third emission order. In addition, we detect incoherent cathodoluminescence that is generated by electrons penetrating the input facet of the fiber and scattering inelastically. As a result, our HSAR measurements reveal a Fano resonance that is coupled to a Rayleigh anomaly of the metagrating, and that overlaps with the angular dispersion of second-order SPR at 20 keV. Our findings demonstrate the potential of optical fiber-integrated metasurfaces as a versatile platform to implement novel ultrafast light sources and to synthesize complex free-electron quantum states with light.