Near-field Imaging Research Articles

Tip-enhanced Raman scattering and near-field optical imaging of semiconducting monolayer and few-layer MoTe2

Transition metal dichalcogenides (TMDs) offer exceptional platforms to study unique phenomena in two-dimensional (2D) materials. The understanding of phonon interactions in atomically thin TMDs is crucial for the development of novel applications. However, advancing our knowledge on phonon dynamics in TMDs under optical, thermal, electric, and strain perturbations requires comprehensive and minimally invasive chemical imaging techniques with nanoscale spatial resolution. Tip-enhanced Raman scattering (TERS) provides new promising avenues towards this goal while also enabling a detailed analysis of structural heterogeneity. Here, we demonstrate gap-mode TERS imaging of mechanically exfoliated monolayer and few-layer 2H–MoTe2. At 633 nm, 671 nm, and 785 nm laser excitations, we report an overwhelmingly selective TERS enhancement of the out-of-plane A1g phonon mode relative to in-plane E12g phonon mode for mono-to few-layer MoTe2. The pure near-field spectral line shapes are clearly distinct from the well-characterized far-field counterparts. We demonstrate that near-field interactions of selective phonon modes are extremely sensitive to the excitation wavelength, sample thickness, and structural defects. Our results therefore provide fundamental information for the nanoscale characterization of semiconducting MoTe2-based devices.

Unveiling the in-plane anisotropic dielectric waveguide modes in α-MoO3 flakes

Abstract The unique in-plane and out-of-plane anisotropy of α-MoO3 has attracted considerable interest for potential optoelectronic applications. However, most research has focused on the mid-infrared spectrum, leaving its properties and applications in the visible and near-infrared light spectrum less explored. This study advances the understanding of waveguiding properties of α-MoO3 by near-field imaging of the waveguide modes along the [100] and [001] directions of α-MoO3 flakes at 633 nm and 785 nm. We investigated the effects of flake thickness and documented the mode's dispersion relationship, which is crucial for tailoring α-MoO3's optical responses in device applications. Our findings enhance the research field of α-MoO3, highlighting its utility in fabricating next-generation optoelectronic devices for its unique optically anisotropic waveguide.

Hybrid Ghost Phonon Polaritons in Thin-Film Heterostructure.

Ghost phonon polaritons (g-PhPs), a unique class of phonon polaritons in the infrared, feature ultralong diffractionless propagation (>20 μm) across the surface and tilted wavefronts in the bulk. Here, we study hybrid g-PhPs in a heterostructure of calcite and an ultrathin film of the phase change material (PCM) In3SbTe2, where the optical field is bound in the PCM film with enhanced confinement compared with conventional g-PhPs. Near-field optical images for hybrid g-PhPs reveal a lemniscate pattern in the momentum distribution. We fabricated In3SbTe2 gratings and investigated how different orientations and periodicities of gratings impact the propagation of hybrid g-PhPs. As the grating period decreases to zero, the wavefront of hybrid g-PhPs can be dynamically steered by varying the grating orientation. Our results highlight the promise of hybrid g-PhPs with tunable functionalities for nanophotonic studies.

Multiplication of the orbital angular momentum of phonon polaritons via sublinear dispersion

Optical vortices (OVs) promise to greatly enhance optical information capacity via orbital angular momentum multiplexing. The need for the on-chip integration of orbital angular momentum technologies has prompted research into subwavelength-confined polaritonic OVs. However, the topological order imprinted by the structure used for transduction from free-space beams to surface polaritons is inherently fixed after fabrication. Here we overcome this limitation via dispersion-driven topological charge multiplication. We switch the OV topological charge within a small frequency range (~3%) by leveraging the strong sublinear dispersion of low-loss surface phonon polaritons on silicon carbide membranes. Applying the Huygens principle, we quantitatively evaluate the topological order of experimental OVs detected by near-field imaging. We further explore the deuterogenic effect, which predicts the coexistence of multiple topological charges in higher-order polaritonic OVs. Our work demonstrates a viable method to manipulate the topological charge of polaritonic OVs, paving the way for the exploration of novel orbital-angular-momentum-enabled light–matter interactions at mid-infrared frequencies.

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.

Near-Field Terahertz Morphological Reconstruction Nanoscopy for Subsurface Imaging of Protein Layers.

Protein layers formed on solid surfaces have important applications in various fields. High-resolution characterization of the morphological structures of protein forms in the process of developing protein layers has significant implications for the control of the layer's quality as well as for the evaluation of the layer's performance. However, it remains challenging to precisely characterize all possible morphological structures of protein in various forms, including individuals, networks, and layers involved in the formation of protein layers with currently available methods. Here, we report a terahertz (THz) morphological reconstruction nanoscopy (THz-MRN), which can reveal the nanoscale three-dimensional structural information on a protein sample from its THz near-field image by exploiting an extended finite dipole model for a thin sample. THz-MRN allows for both surface imaging and subsurface imaging with a vertical resolution of ∼0.5 nm, enabling the characterization of various forms of proteins at the single-molecule level. We demonstrate the imaging and morphological reconstruction of single immunoglobulin G (IgG) molecules, their networks, a monolayer, and a heterogeneous double layer comprising an IgG monolayer and a horseradish peroxidase-conjugated anti-IgG layer. The established THz-MRN presents a useful approach for the label-free and nondestructive study of the formation of protein layers.

A tunable terahertz metasurface with function of polarization conversion and circular dichroism

A tunable and multifunctional electromagnetic control metasurface is proposed. The metasurface comprises the graphene sheet and gold open ring, enabling multiband cross-polarization conversion (CPC) and line-to-circular polarization conversion (LTCPC). Simultaneously, it can achieve circular dichroism (CD) with amplitude of 0.99 at 2.28 THz, along with the capability for near-field imaging. The physical mechanism of operation is studied by analyzing surface current distribution and electric field. In addition, because the metasurface exhibits chiral characteristics, appropriate rotation can induce a phase gradient, facilitating a phase coverage of 0–360°. Based on the tunable phase gradient of metasurface, anomalous reflection and vortex beams can be realized.

Thermally Controlled Broadband Ge2Sb2Te5-Based Metamaterial Absorber for Imaging Applications

In this paper, we theoretically and numerically demonstrate a thermally controlled broadband absorber based on the phase change material Ge2Sb2Te5 (GST). When GST operates in the amorphous state, the proposed metamaterial acts as a broadband nearly perfect absorber. The absorption can reach more than 90% in the wavelength range from 0.9 to 1.41 μm. As an application of the GST-based metamaterial absorber, the near-field imaging effect is achieved by using the intensity difference of optical absorption. Moreover, the thermally controlled switchable imaging can be performed by changing the phase transition characteristics of GST, and the imaging quality and contrast can be adjusted by changing the geometrical parameters. This designed metamaterial may have potential applications in near-infrared temperature control imaging, optical encryption, and information hiding.

Phase distribution and circular dichroism switchable terahertz chiral metasurface.

Chiral metasurfaces have many applications in the terahertz (THz) band, but they still lack modulation flexibility and functionality expansion. This paper presents a terahertz chiral metasurface with switchable phase distribution and switchable circular dichroism (CD). The metasurface unit consists of a metallic inner ring embedded in vanadium oxide and a vanadium oxide outer ring, state switching by thermal control of vanadium oxide and a change in the frequency of the incident wave. Based on the switchable phase distribution, we designed a focusing vortex beam generator with adjustable focal lengths through simulation. Based on the switching CD capability, we simulate its mode switching in near-field imaging using numerical simulation, and innovatively propose an optical encryption method. Utilizing the chiral property, we also designed dual-channel switchable holographic imaging in the same frequency band, which combined with the state change of VO2 can realize a total of 4 holograms switching. Our proposed metasurface is expected to provide new ideas for the study of optical encryption and wavefront modulation of dynamics.

A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception

This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging.

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.

THz near-field intensity distribution imaging in the 0.3 THz band using a highly sensitive polarization CMOS image sensor using a 0.35 μm CMOS process

In this paper, we propose a low-disturbance and fast terahertz (THz) near-field intensity distribution imaging method. The THz detector is fabricated using an oriented multiwalled carbon nanotube (CNT) thin film and a LiNbO3(LN) crystal to the thin film is attached. The CNT absorbs and converts THz waves into heat, and the birefringence change of the LN crystal owing to the heat is used. The birefringence change was measured with high sensitivity using a dual-polarizer configuration of a uniform polarizer and a polarization CMOS image sensor. The fabricated THz detector is a low-disturbance method because it does not use metal, and it can measure the THz distribution in the plane all at once, which is faster than the antenna scanning method. Using the proposed method, we have successfully imaged the THz near-field intensity distribution emitted from an impact avalanche and transit time diode oscillating at 0.278 THz.

Detection of Metal Impurities in Fluids through Microwave Near-Field Imaging

Detection of Metal Impurities in Fluids through Microwave Near-Field Imaging

Integrated convolutional neural networks for joint super-resolution and classification of radar images

Deep learning techniques have been widely used for two-dimensional (2D) and three-dimensional (3D) computer vision problems, such as object detection, super-resolution (SR) and classification to name a few. Radar images suffer from poor resolution as compared to optical images, hence developing a high-accuracy model to solve computer vision problems, such as a classifier, is a challenge. This is because of the lack of high-frequency details in the input images which makes it difficult for the classifier model to generate accurate predictions. Ways of addressing this challenge include training the learning model with a large dataset or using a more complicated model, such as deeper layer architecture. However, employing such approaches might result in the overfitting of the model, where the model might not generalize well on previously unseen data. Also, generating a large dataset for training the model is a challenging task, especially in the case of radar images. An alternate solution for achieving high accuracy in radar classification problems is provided in this paper wherein a CNN-enabled super-resolution (SR) model is integrated with the classifier model. The SR model is trained to generate high-resolution (HR) millimeter-wave (mmW) images from any input low-resolution (LR) mmW images. These resolved images from the SR model will be used by the classifier model to classify the input images into appropriate classes, consisting of threat and non-threat objects. The training data for the dual CNN layers are generated using a numerical model of a near-field coded-aperture computational imaging (CI) system. This trained dual CNN model is tested with simulated data generated from the CI numerical model wherein a high classification accuracy of 95% and a fast inference time of 0.193 s are obtained, making it suitable for real-time automated threat classification applications. For fair comparison, the developed CNN model is also validated with experimentally generated reconstruction data, in which case, a classification accuracy of 94% is obtained.

A Near-Field Imaging Method Based on the Near-Field Distance for an Aperture Synthesis Radiometer

For an aperture synthesis radiometer (ASR), the visibility and the modified brightness temperature (BT) are related to the Fourier transform when the distance between the system and the source is in the far-field region. BT reconstruction can be achieved using G-matrix imaging. However, for ASRs with large array sizes, the far-field condition is not satisfied when performing performance tests in an anechoic chamber due to size limitations. Using far-field imaging methods in near-field conditions can introduce errors in the images and fail to correctly reconstruct the BT. Most of the existing methods deal with visibilities, converting near-field visibilities to far-field visibilities, which are suitable for point sources but not good for extended source correction. In this paper, two near-field imaging methods are proposed based on the near-field distance. These methods enable BT reconstruction in near-field conditions by generating improved resolving matrices: the near-field G-matrix and the F-matrix. These methods do not change the visibility measurements and can effectively image both the point source and the extended source in the near field. Simulations of point sources and extended sources in near-field conditions demonstrate the effectiveness of both methods, with F-matrix imaging outperforming near-field G-matrix imaging. The feasibility of both near-field imaging methods is further validated by carrying out experiments on a 10-element Y-array system.

Fast VGG: An Advanced Pre-Trained Deep Learning Framework for Multi-Layered Composite NDE via Multifrequency Near-Field Microwave Imaging

ABSTRACT Microwave nondestructive testing (NDT) techniques show promise for composite inspection due to microwave signals’ ability to penetrate and interact with internal structures. However, current microwave imaging approaches have poor spatial resolution, struggling to distinguish defects from defect-free regions. This limits reliable subsurface analysis and widespread adoption. This paper introduces a novel multi-frequency microwave imaging fusion method using an open-ended waveguide and deep learning to enhance defect detection accuracy in carbon fiber-reinforced polymer (CFRP) composites. The proposed technique employs an optimized feature extraction strategy to improve differentiation between defective and sound areas for superior subsurface visualization. The method leverages VGG-19 for efficient feature extraction and parallel processing to merge information from multi-frequency images. Our method significantly improved detection accuracy and F1 score, surpassing non-deep learning image fusion techniques by at least 50%. The optimized fusion strategy enables clear visualization of various defect types across multiple subsurface layers. Our approach also reduces computation time versus standard VGG implementation by 3–4×, showing scalability. The results demonstrate the proposed method’s potential to overcome existing constraints and provide rapid, accurate subsurface analysis of complex CFRP structures through an optimized deep learning framework, holding significance for expanding microwave NDE applications.

A historical overview of Nano-optics: From Near-Field Optics to Plasmonics

Abstract Nano-optics is an emergent research field in physics that appeared in the 1980s, which deals with light-matter optical interactions at the nanometer scale. In early studies of nano-optics, the main concern focus was to obtain higher optical resolution over the diffraction limit. The researches of near-field imaging and spectroscopy based on Scanning Near-Field Optical Microscopy (SNOM) were developed. The exploration of improving SNOM probe for near-field detection led to the emergence of surface plasmons. In the sense of resolution and wider application, there has been a significant transition from seeking higher resolution microscopy to plasmonic near-field modulations in the nano-optics community during the nano-optic development. Nowadays, studies of nano-optics prefer the investigation of plasmonics in different material systems. In this article, the history of the development of near-field optics is briefly reviewed. The difficulties of conventional SNOM to achieve higher resolution are discussed. As an alternative solution, surface plasmons have shown the advantages of higher resolution, wider application, and flexible nano-optical modulation for new devices. The typical studies in different periods are introduced and analyzed characteristics of nano-optics in each stage. In this way, the evolution progress from near-field optics to plasmonics of nano-optics research is presented. The future development of nano-optics is discussed then.

Optical nanoimaging of laser-switched phase-change plasmonic infrared antennas.

We investigate the plasmonic properties of laser-printed chalcogenide phase-change material In3SeTb2 (IST) antennas through near-field nanoimaging. Antennas of varying lengths were fabricated by laser switching an amorphous IST film into its crystalline metallic state. Near-field imaging elucidates the pronounced field confinement and enhancement at the antenna extremities along with the emergence of different ordered plasmonic modes with increasing length. Compared to gold antennas, the PCM antennas exhibit slightly lower but still substantial near-field enhancement with greater compactness. The interplay between antenna length, illumination angle, and excitation frequency enables versatile control over the resonant near-field distribution. Our work provides deeper understanding and tunable functionalities of laser-printed PCM nanoantennas for potential applications in compact, dynamically reconfigurable nanophotonic devices.

Terahertz bi-functional polarization converter based on interference mechanism supported by diatomic metasurfaces

Polarization manipulation based on the Jones matrix facilitates the enhancement of light-matter interactions. Recently, arbitrarily tailorable polarization states generated with the assistance of a diatomic metasurface effectively reduce the complexity of the system. Nevertheless, a single polarization switching behavior hinders the application of meta-platforms in cryptographic imaging. Here, we theoretically propose and design a single-layer diatomic all-dielectric metasurface working in the terahertz band, which can efficiently realize bi-functional polarization switching according to the Jones matrix. Such a meta-platform is assembled from two anisotropic silicon pillars with carefully optimized lateral dimensions and in-plane twist angles. Benefiting from the flexible assembly of half-wave plate and quarter-wave plate, the polarization states generated by the constructed metasurfaces in the transmission mode can be arbitrarily tailored. The feasibility of this diatomic metasurface is further validated by a broadband near-field imaging device, paving the way for broader system applications in cryptographic imaging, data storage, and chiral sensing.

Fabrication of oscillating metal probe for dynamic scanning microwave microscopy

Abstract A microwave probe plays a critical role in near-field imaging, and there is a continuous effort to develop them through straightforward methods. This study designed and fabricated an oscillating metal probe and used it for scanning microwave imaging of micro-nano structures. The surface smoothness of the cantilever is approximately 19.3 nm after polishing with diamond abrasive paper, and the tip radius is less than 20 nm using electrochemical etching. The impact of metal electrode materials on microwave signals was assessed in the frequency range of 1–20 GHz. The microwave imaging capability of the devised probe was explored through the imaging of a micro-nano structure. The spatial resolution of microwave imaging reached 0.5 μm over a scanning area of 50 μm × 50 μm. This study has far-reaching significance for developing higher-performance microwave probes and advancing scanning microwave microscopy.