Terahertz Imaging Research Articles

Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection

Abstract Composite materials find increasing applications in modern industry and transport. However, they may lose desirable mechanical properties due to external mechanical excitations, ultraviolet radiation, moisture penetration, or other factors. Therefore, an effective way to assess the condition of materials is necessary. In this article, a nondestructive evaluation of glass fiber-reinforced composite subjected to five-stage static bending is presented. For this reason, pulsed excitation terahertz imaging was utilized, and a data processing/exploration scheme was proposed. The proposed, novel approach consists of an efficient data registration algorithm based on surface approximation (for surface roughness and unevenness elimination) and a parametrization scheme applied for the signals gained from the time response of the glass fiber-reinforced polymer layer. Obtained parameters enable the global description of the evaluated material state and prediction of failure effectively, even in the early stages of destruction.

Colossal room-temperature non-reciprocal Hall effect.

Non-reciprocal charge transport has gained significant attention due to its potential in exploring quantum symmetry and its promising applications. Traditionally, non-reciprocal transport has been observed in the longitudinal direction, with non-reciprocal resistance being a small fraction of the ohmic resistance. Here we report a transverse non-reciprocal transport phenomenon featuring a quadratic current-voltage characteristic and divergent non-reciprocity, termed the non-reciprocal Hall effect. This effect is observed in microscale Hall devices fabricated from platinum (Pt) deposited by a focused ion beam on silicon substrates. The transverse non-reciprocal Hall effect arises from the geometrically asymmetric scattering of textured Pt nanoparticles within the focused-ion-beam-deposited Pt structures. Notably, the non-reciprocal Hall effect generated in focused-ion-beam-deposited Pt electrodes can propagate to adjacent conductors such as Au and NbP through Hall current injection. Additionally, this pronounced non-reciprocal Hall effect facilitates broadband frequency mixing. These findings not only validate the non-reciprocal Hall effect concept but also open avenues for its application in terahertz communication, imaging and energy harvesting.

A novel enhancing method for terahertz imaging of integrated circuits flaw detection

This research paper addresses the current gap in understanding the intricate damage types within packaged integrated circuits (ICs). We introduce a novel and multiscale latent low-rank representation (M-LatLRR) methodology, meticulously tailored to enhance the quality of terahertz (THz) IC images and reveal latent features. By integrating THz imaging with our proposed M-LatLRR method, we aim to facilitate the precise identification of damage types within inside the packaged ICs. Firstly, the multiscale Gaussian functions are used to remove the blur and the components are obtained via averaging. Secondly, the image matrices are utilized to extract base matrices and detail matrices. With LatLRR, the multiscale detail matrices are extracted at several representation levels. The final enhanced image is reconstructed by average strategy for dealing with the detail and pre-enhancement parts. The M-LatLRR framework is universal and can be effectively applied to extract multi-level features of packaged IC images. In a comparative analysis, our method demonstrates superior capabilities in determining the failure types of fractured wire bonds, cracks, and delamination of the dielectric layer, outperforming alternative methodologies.

Scanless Spectral Imaging of Terahertz Vortex Beams Generated by High‐Resolution 3D‐Printed Spiral Phase Plates

Terahertz technology has experienced significant advances in the past years, leading to new applications in the fields of spectroscopy, imaging, and communications. This progress requires the development of dedicated optics to effectively direct, control and manipulate terahertz radiation. In this regard, 3D printing technologies have shown great potential, offering fast prototyping, high design flexibility, and good reproducibility. While traditional 3D printing techniques allow for the preparation of terahertz optical components operating at relatively low frequencies (<0.4 THz) due to their limited resolution, two‐photon polymerization lithography (TPL) exhibits high detail resolution and low surface roughness and can thus potentially enable the fabrication of high‐frequency terahertz devices. Here, as a proof of principle, spiral phase plates operating at 1 THz are designed and fabricated by means of TPL. Moreover, these samples are characterized via a rapid and scanless terahertz imaging technique customized to obtain a coherent hyperspectral analysis of the generated vortex beams at varying distances along propagation. Numerical simulations are also conducted for comparison with experiments, revealing a good agreement. Current limitations of the technique are found to be mainly related with terahertz loss in TPL polymers, and possible solutions are discussed.

InAs Terahertz Metalens Emitter for Focused Terahertz Beam Generation

Metasurfaces have opened doors to combining multiple photonic functionalities in a single compact device. In particular, the ability to generate short terahertz (THz) pulses with precise wavefront engineering in a single THz metasurface redefined the role metasurfaces can play in THz systems. Here, an InAs metalens emitter which generates and focuses a THz pulse beam is demonstrated using a 130 nm thick InAs metasurface designed as a binary‐phase Fresnel zone plate. The THz beam is focused to a spot of ≈430 μm at 1 THz with a short focal length of 5 mm and large numerical aperture of 0.5. Nanoscale InAs Mie resonators comprising the metasurface enable THz generation with an amplitude as high as 20 times compared to plasmonic THz emitters and several times compared to a 1 mm thick ZnTe crystal. This InAs metasurface emitter provides a new paradigm for designing THz imaging, spectroscopy, and communication systems, where THz beam generation and shaping are performed with a single device without compromising the generation efficiency, while eliminating losses and avoiding limitations of phase matching of conventional nonlinear optics approaches.

Self-supervised learning-based re-parameterization instance segmentation for hazardous object in terahertz image

Recently, terahertz imaging technology has shown widespread potential applications in the public security screening field. Previous terahertz hazardous object detection techniques primarily relied on manual recognition and image processing methods. Some solutions incorporating deep learning technologies depend on large quantities of high-quality data, making it challenging to achieve low-cost and high-performance detection. To solve the issue of dependency on large amounts of high-quality data, we propose a diversified dynamic structure You Only Look Once (DDS-YOLO) model based on masked image modeling and structural reparameterization for the instance segmentation of hazardous objects in terahertz security inspection images. To address the scarcity and annotating difficulty problems of terahertz security inspection image samples, we combine automatic data strategies with masked image modeling for self-supervised learning. We propose a multilevel feature refinement fusion mechanism to enhance the quality of learned feature representations. The backbone parameter transfer and fine-tuning training strategies are employed to achieve hazardous object instance segmentation on the terahertz dataset. To address the low detection accuracy issue caused by the poor terahertz image quality, we develop a reparameterizable hierarchical structure for the backbone, improve a multi-scale feature-integrating neck, and design a dynamically decoupled head with lower computational requirements to enhance the performance of the instance segmentation model. Experimental results demonstrate that the proposed model accurately outputs detection boxes, categories, and segmentation masks for hazardous objects with minimal training samples. The comparative experimental results indicate that the proposed model outperforms existing state-of-the-art methods in terms of detection performance. The proposed DDS-YOLO model achieves 59.3 % in mask mean Average Precision (mAP) and 61.3 % in box mAP, and the model parameters and computational requirements also meet practical application scenarios.

Feasibility and potential of terahertz spectral and imaging technology for Apple Valsa canker detection: A preliminary investigation

Apple Valsa canker (AVC) caused by the Ascomycete Valsa mali, seriously constrains the production and quality of apple fruits. The symptomless incubation characteristics of Valsa mali make it highly challenging to detect AVC at an early infection stage. After infecting the wound of apple bark, the pathogenic hyphae of AVC will expand and colonize the phloem tissue. Meanwhile, various enzymes and toxic substances released by hyphae cause the decomposition of cellulose and lignin, and the generation of poisonous secondary metabolites in bark tissue. However, these early symptoms of AVC are invisible from the bark’s appearance. Fortunately, Terahertz Spectral Imaging (ThzSI) technology with the advantage of penetrating, and fingerprinting is promising for detecting hidden or slight symptoms of the fungal infection. This study is a preliminary investigation of terahertz frequency-domain spectra for AVC in the early stage of infection. Healthy and two-week-infected apple tree branches were prepared for capturing ThzS images, and the spectral data were preprocessed by Multivariate scattering correction (MSC), Savitzky-Golay convolution smoothing (SG), and standard normal variate (SNV) respectively to remove data noise and improve data quality. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and random frog (RFROG) were employed to extract the spectral feature bands to eliminate redundant data and improve computational efficiency. Machine learning models were established based on the spectral features to detect AVC at an early infection stage, where 11 of them exhibited the best performance with F1-score of 99.72%. To further explore disease information in spatial spectra, imaging data were acquired using terahertz imaging technology. Based on imaging data, pseudo-color imaging, histogram equalization, and Otsu segmentation were employed to visualize early infection areas in apple barks. Furthermore, histogram feature (HF), shape feature (SF), and local binary pattern (LBP) extracted from terahertz spectral images were utilized to establish the SVM, RF, and KNN models. HF-SF-KNN and HF-SF-LBP-KNN with the best performance achieved F1-score of 98.82%. This study presents a preliminary application of terahertz spectral and imaging technology for early-stage AVC detection and demonstrates its feasibility. Additionally, it provides a new way to detect AVC, which expands the application of ThzSI technology in tree disease detection in orchards and lays the foundation for further research.

Large depth-of-focus achievement based on an aspheric lens with a ring

Terahertz (THz) imaging technology has been widely studied because of its easy penetration of non-polar materials and low photon energy. To acquire a beam featuring both excellent transverse spatial resolution and a considerable depth-of-focus (DOF) to fulfill the demands of two-dimensional and three-dimensional THz imaging, this paper presents an aspheric lens with ring (ALR). The ALR has a controlled diameter of 50 mm, can be machined by 3D-printed technology, and does not need to use complex imaging optical paths to achieve the large DOF function. In a transmitted point-scan imaging system with a 140 GHz light source, the lens can achieve both a resolution of 6 mm and an effective DOF of 66.4 mm for objects greater than 27 mm from the lens surface.

Cryogenically cooled periodically poled lithium niobate wafer stacks for multi-cycle terahertz pulses

We report on the generation of high-power narrow-bandwidth terahertz (THz) pulses by cryogenic cooling of hand-made periodically poled lithium niobate (PPLN) wafer stacks. As a proof-of-concept, we cool stacks with up to 48 wafers down to 97 K and achieve few-percent bandwidths at a center frequency of 0.39 THz, with pulse energy up to 0.42 mJ and average power of 21 mW. Supported by modeling, we observe effective cooling of PPLN wafer stacks that not only reduces terahertz absorption but critically maintains the micrometer-scale inter-wafer gaps for optimal terahertz transmission. Our results unlock the potential for scaling these large-area sources to greater numbers of wafers to push both the energy and bandwidth beyond current capability, opening up possibilities in areas such as terahertz-driven particle acceleration, terahertz imaging, and control over material properties.

Integrated identification and detection of hydration state and its evolution using terahertz technology

The accurate detection of dehydration processes in hydrated drugs can reveal various intermolecular vibration modes mediated by hydrogen bonds between water molecules and other components, which underpin the further development of pharmaceutical science, food safety and biophysics. Herein, terahertz (THz) technology is utilized to investigate the dehydration state of d(+)-Raffinose pentahydrate (Rf·5H2O), in conjunction with imaging-based point by point scanning data acquisition and barcodes methods, to establish an innovative platform integrated identification, trace detection, and application capabilities. Our study demonstrates that the dehydration process of Rf·5H2O can be dynamically monitored through the evolution of its THz absorption peaks, offering more precise results compared to XRD and Raman spectroscopies. Moreover, the absorbance spectra data collected at each individual pixel is utilized to build visualized THz images, achieving an ultralow minimum content required for detection of 0.032 μg/(50 μm)2. Additionally, we introduce a THz spectra-barcode conversion system that not only ensures efficient electronic recordkeeping but also enhances user readability, thereby facilitating the practical applications of THz technology.

Research on wheat impurity identification method based on terahertz imaging technology

The traditional detection of impurities in wheat has difficulties such as low precision, time-consuming, and cumbersome, therefore, it is important to study the method of rapid and accurate detection of impurities in wheat for correctly assessing the quality grade of wheat. Terahertz (THz) technology has many superior properties such as transient, broadband, low-energy, and penetrating, which can realize rapid and nondestructive detection of wheat quality. In this study, a classification and recognition algorithm AHA-RetinaNet-X for wheat impurity terahertz images based on RetinaNet and Artificial hummingbird algorithm (AHA) is proposed.A THz three-dimensional tomography imaging system is used to image wheat and its impurities, and two THz image datasets, respectively the wheat and impurity dataset for verifying the classification effect of wheat and impurities and the impurity dataset for verifying the classification effect of impurities. The experimental results show that the AHA-RetinaNet-X model outperforms other detection and classification models in terms of accuracy, F1-score, precision, recall, and specificity, and is able to achieve 96.1%, 94.9%, 95.2%, 95.8%, 95.5%, 95.3%, and 93.3% for the wheat and impurity dataset and the impurity dataset, respectively, 95.6%, 96.3%, and 95.2%, and the mAP value of AHA-RetinaNet-X is also higher than the other models and can reach 92.1%. The combination of THz imaging technology and AHA-RetinaNet-X can realize the classification and identification of wheat and impurities, which provides a new method for the non-contact rapid nondestructive detection and identification of wheat and impurities, and also provides a reference for the research of the identification and detection methods of other substances.

High gain MIMO antenna with multiband characterization for terahertz applications

Recently, the relevance of multiple input multiple output (MIMO) technology has increased due to the growing user base and the surge in demand for high data rates. It provides an essential answer to the pressing requirement for high-capacity communication systems compliant with new standards. In this study, a four-element MIMO antenna with specific properties—a thickness of 0.0224 mm and a relative permittivity of 4.3 —is presented on a polyamide substrate. It is intended to operate in terahertz (THz) applications at 0.395 and 0.629 THz. The configuration of the antenna is based on a traditional fork antenna. At the resonant frequencies, this MIMO antenna can achieve bandwidths of 9.53 GHz and 24.19 GHz, with reflection coefficients of -16.19 dB and -24.27 dB, respectively. Moreover, a gain of 5.17 dB and 3.19 dB are obtained at the second and first resonant frequencies, respectively. In the effort to maintain the operational band and enhance gain, a significant challenge arises from the insufficient isolation between the four antenna elements within the limited space. A Defected Ground Structure (DGS) is constructed and the inter-element distance is carefully investigated in order to effectively address this issue. Mutual coupling effects are effectively reduced with this antenna, and an amazing MIMO diversity response is shown. Additionally, it achieves a mean effective gain of less than -3, envelope correlation coefficients below 0.0125, diversity gain of around 10 dB, and total active reflection coefficients below -4 dB. The suggested design was simulated using the ansys High-Frequency Structure Simulator program. These enhancements show that the recommended MIMO antenna is a suitable choice for terahertz applications like 5 G networks of the future and terahertz imaging systems used in medical settings for tissue imaging and cancer diagnosis.

A novel framework for concealed weapons detection using passive terahertz imaging

ABSTRACT In this work, a novel framework for non-destructive, non-intrusive and completely automatic concealed weapon detection under human clothing for entry control and security check applications using passive terahertz (THz) images has been given. The technique models the problem of concealed weapon detection as that of binary segmentation, where the pixels corresponding to concealed weapons are foreground while the remaining are background. The core idea of the proposed framework is to first oversegment the THz image into superpixels and subsequently multiple handcrafted features are extracted from each superpixel. Finally, a machine learning-based classifier is used to perform binary classification thereby classifying each superpixel as foreground or background such that each pixel in a foreground superpixel is a foreground pixel and vice-versa for background. It is worth mentioning that this technique does not require a perfect segmentation to extract features from, rather an oversegmentation suffices. Furthermore, both global features like those based on image saliency and local features like those based on intensity are extracted considering the image properties. The detailed experiments and comparative analysis confirm that the proposed technique efficiently detects concealed weapons, exhibiting superior performance compared to state-of-the-art binary segmentation methods for the purpose.

A tunable chiral metasurface useable in terahertz imaging and wavefront shaping

We proposed a tunable chiral metasurface comprising a reflective bottom layer of gold, a dielectric layer of polyimide, and a structural top layer of gold-graphene. Its main properties were studied via numerical simulations conducted using CST Studio Suite. The results indicate that, based on the chiral metasurface, we achieved dual-band circular dichroism of −0.5 and 0.77 at 0.9 THz and 1.06 THz, respectively, and complementary near-field imaging applications were realized by tuning the Fermi level (E f ) of graphene. Subsequently, exploiting the exceptional selective characteristics of circularly polarized waves using a chiral metasurface, eight chiral phase-gradient metasurfaces were constructed by rotating the chiral structure. Moreover, based on the Pancharatnam-Berry phase principle, tunable wavefront shaping applications were further realized, including anomalous reflection, vortex beams, and focusing. In anomalous reflection, the reflection angles for left-circularly polarized (LCP) and right-circularly polarized (RCP) incidences are opposite when adjusting the E f of graphene. For example, when the graphene E f is 0 eV and the LCP wave is incident at 0°, the reflection angle is −18°. Conversely, when the graphene E f is 1 eV and the RCP wave is incident at 0°, the reflection angle is 18°. In the application of vortex beams, by adjusting the E f of graphene, we achieved vortex beams with opposite topological charges under different circularly polarized incidences. In the focusing application, the incident LCP and RCP can achieve focusing and defocusing, respectively. And the graphene E f can dynamically control the focusing efficiency at the incident LCP, increasing it from 13.63% to 44.84%.

Research on Non-Destructive Quality Detection of Sunflower Seeds Based on Terahertz Imaging Technology.

The variety and content of high-quality proteins in sunflower seeds are higher than those in other cereals. However, sunflower seeds can suffer from abnormalities, such as breakage and deformity, during planting and harvesting, which hinder the development of the sunflower seed industry. Traditional methods such as manual sensory and machine sorting are highly subjective and cannot detect the internal characteristics of sunflower seeds. The development of spectral imaging technology has facilitated the application of terahertz waves in the quality inspection of sunflower seeds, owing to its advantages of non-destructive penetration and fast imaging. This paper proposes a novel terahertz image classification model, MobileViT-E, which is trained and validated on a self-constructed dataset of sunflower seeds. The results show that the overall recognition accuracy of the proposed model can reach 96.30%, which is 4.85%, 3%, 7.84% and 1.86% higher than those of the ResNet-50, EfficientNeT, MobileOne and MobileViT models, respectively. At the same time, the performance indices such as the recognition accuracy, the recall and the F1-score values are also effectively improved. Therefore, the MobileViT-E model proposed in this study can improve the classification and identification of normal, damaged and deformed sunflower seeds, and provide technical support for the non-destructive detection of sunflower seed quality.

Resolution enhancement in terahertz imaging with multi-wavelength information

Thanks to the unique characteristics of terahertz waves, terahertz imaging has become one of the promising imaging technologies. However, due to the weak signal source and strong diffraction of terahertz waves, terahertz imaging has a significant amount of noise, which makes it is challenge to achieve satisfactory clarity in images. In this work, we propose an algorithm that uses multi-wavelength information to improve the resolution of terahertz imaging. The resolvability of the images has been improved by at least 1.4 times, and the noise has been effectively filtered out. This algorithm enhances the image resolution without requiring any hardware upgrades, benefiting the terahertz imaging system with multi-wavelength imaging capabilities.

A circularly polarized graphene based wideband 1 × 2 array antenna for terahertz spectrum applications

A graphene-based 1 × 2 array antenna with circular polarization for terahertz applications is prescribed in this article. Initially, a novel concept of a folded quarter wave impedance transformer is utilized in the design process of a single element for minimizing the overall antenna size. The opposite corners of the patches have been truncated and structural modifications are performed with the insertion of four flower-shaped slots along with an additional circular slot for achieving a much-improved reflection coefficient and better impedance bandwidth. It also shows a much wider 3 dB axial ratio bandwidth, confirming circular polarization due to the suggested modifications in its geometry. Then, an array antenna has been formed to provide better gain. The configured patches are fed by a magic-T power divider to attain the required impedance matching. The results of the CP antenna array have been analyzed using the HFSS and CST simulators. The propounded 1 × 2 array antenna shows circular polarization with a 3 dB AR bandwidth of 205 GHz (2.345–2.55 THz) and wide spectral coverage of 210 GHz (2.345 − 2.555 THz) along with a maximum gain of 8.65 dB and 99.8 % radiation efficiency with a total size of 53.5 × 102 × 1.56 μm3. It could be utilized for high-speed data transmission, material characterization, terahertz spectroscopy, terahertz imaging, etc. applications.

Graphene metamaterial (GMM) based dual mode, tunable, and broadband THz absorber with triple circular split ring structure

In this study we investigated a novel approach to designing a graphene metamaterial (GMM) based terahertz (THz) absorber with dual-mode functionality, tunability, and broadband absorption capabilities. The study leverages the unique properties of graphene, a material known for its exceptional electronic and optical characteristics, combined with metamaterials to achieve efficient THz absorption. Here we performed extensive simulation on four different types of configurations and found the optimized structure has the highest bandwidth of 3.8 THz and absorption over 90 %. The absorber is designed to operate in two distinct modes, enhancing its versatility for different applications in the THz spectrum. Moreover, the tunability of the absorber is a significant feature, allowing for dynamic adjustment of the absorption frequency, which is crucial for applications in THz imaging, sensing, and communication systems. The broadband nature of the absorber ensures effective performance over a wide range of frequencies, addressing the need for flexible and high-performance devices in emerging THz technologies. This work represents a significant advancement in the field of THz metamaterials, with potential implications for the development of next-generation THz devices.

Triple-frequency terahertz modulator based on electronically controllable metamaterial from the coupling effect

This paper proposes a triple-frequency terahertz amplitude modulator that utilizes an I-shaped strip and four U-shaped metal patches within a common metal-substrate configuration. The top metal layer consists of an I-shaped strip and four U-shaped metal patches, while the bottom substrate layer is made of polyimide. Amplitude modulation is achieved through adjusting the plasma frequency of the high-electron-mobility transistors, resulting in a modulation depth of nearly 93% at resonance frequencies of 0.26 and 0.49 THz. At 0.6 THz, the modulation depth reaches 65%, demonstrating excellent performance. Resonance frequencies are determined by electric field and surface current distribution. The triple-frequency terahertz amplitude modulator is applicable in various fields, including terahertz communications and imaging.

Root phenotype detection of rice seedling under nitrogen conditions based on terahertz imaging technique

Root phenotype detection is the basis for screening of dominant root and improving of seed breeding. The efficiency of root phenotype detection is restricted by the concealment and complexity of crop roots. In this paper, a new method based on terahertz imaging technology was proposed to detect the phenotypic characteristics of rice root and quantitatively predict root nitrogen content. First, the terahertz imaging spectral data of rice root were preprocessed with de-overlapping, reconstruction, enhancement, and segmentation. Then, the phenotypic characteristics of root length, diameter and surface area of rice roots were extracted according to the refinement algorithm. In addition, three linear regression models were fitted between root phenotype and root nitrogen content. Finally, Convolutional Neural Network (CNN), Genetic Algorithm-Back Propagation Neural Network (GA-BPNN), and Sparrow Search Algorithm-Support Vector Regression (SSA-SVR) were used to predict nitrogen content in rice roots by training and testing terahertz time domain data. Compared with two kinds of root phenotypic analysis software, the average errors of root length, diameter and root surface area calculated in this paper were 10.68 %, 7.29 % and 3.49 %, respectively. The fitting determination coefficients between root length, root surface area and the root nitrogen content were 0.88 and 0.87 after removing three groups of high-nitrogen data and one group of contrast data. The prediction accuracy of SSA-SVR model was 0.99 and the root mean square error was 0.05. Studies show that terahertz imaging technique can provide an effective analytical way for qualitative analysis of root phenotype.