Search

X-ray nanotomography is a powerful tool for the characterization of nanoscale materials and structures, but it is difficult to implement due to the competing requirements of X-ray flux and spot size. Due to this constraint, state-of-the-art nanotomography is predominantly performed at large synchrotron facilities. We present a laboratory-scale nanotomography instrument that achieves nanoscale spatial resolution while addressing the limitations of conventional tomography tools. The instrument combines the electron beam of a scanning electron microscope (SEM) with the precise, broadband X-ray detection of a superconducting transition-edge sensor (TES) microcalorimeter. The electron beam generates a highly focused X-ray spot on a metal target held micrometers away from the sample of interest, while the TES spectrometer isolates target photons with a high signal-to-noise ratio. This combination of a focused X-ray spot, energy-resolved X-ray detection, and unique system geometry enables nanoscale, element-specific X-ray imaging in a compact footprint. The proof of concept for this approach to X-ray nanotomography is demonstrated by imaging 160 nm features in three dimensions in six layers of a Cu-SiO2 integrated circuit, and a path toward finer resolution and enhanced imaging capabilities is discussed. [ABSTRACT FROM AUTHOR]

Purpose: We investigated the characteristics of radiation-induced current in nano-porous pellet and thin-film anodized tantalum exposed to kVp X-ray beams. We aim at developing a large area (≫cm2) thin-film radiation sensor for medical, national security and space applications. Methods: Large area (few cm2) micro-thin Ta foils were anodized and coated with a counter electrode made of conductive polymer. In addition, several types of commercial electrolytic porous tantalum capacitors were assembled and prepared for irradiation with kVp X-rays. We measured dark current (leakage) as well as transient radiation-induced currents as a function of external voltage bias. Results: Large transient currents (up to 50 nA) under X-ray irradiation (dose rate of about 3 cGy/s) were measured in Ta2O5 capacitors. Small nano-porous Ta and large-area flat Ta foil capacitors show similar current–voltage characteristic curve after accounting for different X-ray attenuation in capacitor geometry. The signal is larger for thicker capacitor oxide. A non-negligible signal for null external voltage bias is observed, which is explained by fast electron production in Ta foils. Conclusions: Anodized tantalum is a promising material for use in large-area, self-powered radiation sensors for X-ray detection and for energy harvesting. [ABSTRACT FROM AUTHOR]

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network's learning rate to derive the optimal group's weight information and ultimately improve the model's detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time. [ABSTRACT FROM AUTHOR]

Identifying the classes and locations of prohibited items is the target of security inspection. However, X-ray security inspection images with insufficient feature extraction, imbalance between easy and hard samples, and occlusion lead to poor detection accuracy. To address the above problems, an object-detection method based on YOLOv8 is proposed. Firstly, an ASFF (adaptive spatial feature fusion) and a weighted feature concatenation algorithm are introduced to fully extract the scale features from input images. In this way, the model can learn further details in training. Secondly, CoordAtt (coordinate attention module), which belongs to the hybrid attention mechanism, is embedded to enhance the learning of features of interest. Then, the slide loss function is introduced to balance the simple samples and the difficult samples. Finally, Soft-NMS (non-maximum suppression) is introduced to resist the conditions containing occlusion. The experimental result shows that mAP (mean average precision) achieves 90.2%, 90.5%, 79.1%, and 91.4% on the Easy, Hard, and Hidden sets of the PIDray and SIXray public test set, respectively. Contrasted with original model, the mAP of our proposed YOLOv8n model increased by 2.7%, 3.1%, 9.3%, and 2.4%, respectively. Furthermore, the parameter count of the modified YOLOv8n model is roughly only 3 million. [ABSTRACT FROM AUTHOR]

Hybrid pixel detectors have become indispensable at synchrotron and X-ray free-electron laser facilities thanks to their large dynamic range, high frame rate, low noise, and large area. However, at energies below 3 keV, the detector performance is often limited because of the poor quantum efficiency of the sensor and the difficulty in achieving single-photon resolution due to the low signal-to-noise ratio. In this paper, we address the quantum efficiency of silicon sensors by refining the design of the entrance window, mainly by passivating the silicon surface and optimizing the dopant profile of the n+ region. We present the measurement of the quantum efficiency in the soft X-ray energy range for silicon sensors with several process variations in the fabrication of planar sensors with thin entrance windows. The quantum efficiency for 250 eV photons is increased from almost 0.5% for a standard sensor to up to 62% as a consequence of these developments, comparable to the quantum efficiency of backside-illuminated scientific CMOS sensors. Finally, we discuss the influence of the various process parameters on quantum efficiency and present a strategy for further improvement. [ABSTRACT FROM AUTHOR]

X-ray images are an important industrial non-destructive testing method. However, the contrast of some weld seam images is low, and the shapes and sizes of defects vary greatly, which makes it very difficult to detect defects in weld seams. In this paper, we propose a gray value curve enhancement (GCE) module and a model specifically designed for weld defect detection, namely WD-YOLO. The GCE module can improve image contrast to make detection easier. WD-YOLO adopts feature pyramid and path aggregation designs. In particular, we propose the NeXt backbone for extraction and fusion of image features. In the YOLO head, we added a dual attention mechanism to enable the model to better distinguish between foreground and background areas. Experimental results show that our model achieves a satisfactory balance between performance and accuracy. Our model achieved 92.6% mAP@0.5 with 98 frames per second. [ABSTRACT FROM AUTHOR]

Kaonic atom X-ray spectroscopy is a consolidated technique for investigations on the physics of strong kaon–nucleus/nucleon interaction. Several experiments have been conducted regarding the measurement of soft X-ray emission (<20 keV) from light kaonic atoms (hydrogen, deuterium, and helium). Currently, there have been new research activities within the framework of the SIDDHARTA-2 experiment and EXCALIBUR proposal focusing on performing precise and accurate measurements of hard X-rays (>20 keV) from intermediate kaonic atoms (carbon, aluminum, and sulfur). In this context, we investigated cadmium–zinc–telluride (CdZnTe or CZT) detectors, which have recently demonstrated high-resolution capabilities for hard X-ray and gamma-ray detection. A demonstrator prototype based on a new cadmium–zinc–telluride quasi-hemispherical detector and custom digital pulse processing electronics was developed. The detector covered a detection area of 1 cm2 with a single readout channel and interesting room-temperature performance with energy resolution of 4.4% (2.6 keV), 3% (3.7 keV), and 1.4% (9.3 keV) FWHM at 59.5, 122.1, and 662 keV, respectively. The results from X-ray measurements at the DAΦNE collider at the INFN National Laboratories of Frascati (Italy) are also presented with particular attention to the effects and rejection of electromagnetic and hadronic background. [ABSTRACT FROM AUTHOR]

In recent years, automatic detection of threats in X-ray baggage has become important in security inspection. However, the training of threat detectors often requires extensive, well-annotated images, which are hard to procure, especially for rare contraband items. In this paper, a few-shot SVM-constraint threat detection model, named FSVM is proposed, which aims at detecting unseen contraband items with only a small number of labeled samples. Rather than simply finetuning the original model, FSVM embeds a derivable SVM layer to back-propagate the supervised decision information into the former layers. A combined loss function utilizing SVM loss is also created as the additional constraint. We have evaluated FSVM on the public security baggage dataset SIXray, performing experiments on 10-shot and 30-shot samples under three class divisions. Experimental results show that compared with four common few-shot detection models, FSVM has the highest performance and is more suitable for complex distributed datasets (e.g., X-ray parcels). [ABSTRACT FROM AUTHOR]

Perovskite CsPbBr3 semiconductors exhibit unusually high defect tolerance leading to outstanding and unique optoelectronic properties, demonstrating strong potential for γ-radiation and X-ray detection at room temperature. However, the total dose effects of the perovskite CsPbBr3 must be considered when working in a long-term radiation environment. In this work, the Schottky type of perovskite CsPbBr3 detector was fabricated. Their electrical characteristics and γ-ray response were investigated before and after 60Co γ ray irradiation with 100 and 200 krad (Si) doses. The γ-ray response of the Schottky-type planar CsPbBr3 detector degrades significantly with the increase in total dose. At the total dose of 200 krad(Si), the spectral resolving ability to γ-ray response of the CsPbBr3 detector has disappeared. However, with annealing at room temperature for one week, the device's performance was partially recovered. Therefore, these results indicate that the total dose effects strongly influence the detector performance of the perovskite CsPbBr3 semiconductor. Notably, it is concluded that the radiation-induced defects are not permanent, which could be mitigated even at room temperature. We believe this work could guide the development of perovskite detectors, especially under harsh radiation conditions. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19), which emerged as a pandemic, has engulfed so many lives and affected millions of people across the world since December 2019. Although this disease is under control nowadays, yet it is still affecting people in many countries. The traditional way of diagnosis is time taking, less efficient, and has a low rate of detection of this disease. Therefore, there is a need for an automatic system that expedites the diagnosis process while retaining its performance and accuracy. Artificial intelligence (AI) technologies such as machine learning (ML) and deep learning (DL) potentially provide powerful solutions to address this problem. In this study, a state-of-the-art CNN model densely connected squeeze convolutional neural network (DCSCNN) has been developed for the classification of X-ray images of COVID-19, pneumonia, normal, and lung opacity patients. Data were collected from different sources. We applied different preprocessing techniques to enhance the quality of images so that our model could learn accurately and give optimal performance. Moreover, the attention regions and decisions of the AI model were visualized using the Grad-CAM and LIME methods. The DCSCNN combines the strength of the Dense and Squeeze networks. In our experiment, seven kinds of classification have been performed, in which six are binary classifications (COVID vs. normal, COVID vs. lung opacity, lung opacity vs. normal, COVID vs. pneumonia, pneumonia vs. lung opacity, pneumonia vs. normal) and one is multiclass classification (COVID vs. pneumonia vs. lung opacity vs. normal). The main contributions of this paper are as follows. First, the development of the DCSNN model which is capable of performing binary classification as well as multiclass classification with excellent classification accuracy. Second, to ensure trust, transparency, and explainability of the model, we applied two popular Explainable AI techniques (XAI). i.e., Grad-CAM and LIME. These techniques helped to address the black-box nature of the model while improving the trust, transparency, and explainability of the model. Our proposed DCSCNN model achieved an accuracy of 98.8% for the classification of COVID-19 vs normal, followed by COVID-19 vs. lung opacity: 98.2%, lung opacity vs. normal: 97.2%, COVID-19 vs. pneumonia: 96.4%, pneumonia vs. lung opacity: 95.8%, pneumonia vs. normal: 97.4%, and lastly for multiclass classification of all the four classes i.e., COVID vs. pneumonia vs. lung opacity vs. normal: 94.7%, respectively. The DCSCNN model provides excellent classification performance consequently, helping doctors to diagnose diseases quickly and efficiently. [ABSTRACT FROM AUTHOR]

The technique for target detection based on a convolutional neural network has been widely implemented in the industry. However, the detection accuracy of X-ray images in security screening scenarios still requires improvement. This paper proposes a coupled multi-scale feature extraction and multi-scale attention architecture. We integrate this architecture into the Single Shot MultiBox Detector (SSD) algorithm and find that it can significantly improve the effectiveness of target detection. Firstly, ResNet is used as the backbone network to replace the original VGG network to improve the feature extraction capability of the convolutional neural network for images. Secondly, a multi-scale feature extraction (MSE) structure is designed to enrich the information contained in the multi-stage prediction feature layer. Finally, the multi-scale attention architecture (MSA) is fused onto the prediction feature layer to eliminate the redundant features' interference and extract effective contextual information. In addition, a combination of Adaptive-NMS and Soft-NMS is used to output the final prediction anchor boxes when performing non-maximum suppression. The results of the experiments show that the improved method improves the mean average precision (mAP) value by 7.4% compared to the original approach. New modules make detection much more accurate while keeping the detection speed the same. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability. [ABSTRACT FROM AUTHOR]

As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease screening, detection, and disease tracking. This study exploits the ability of sparse representation to learn sparse transformations of information and combines it with image decomposition theory. The structural information of low-dose CT images is separated from noise and artifact information, and the sparse expression of sparse transformation is used to improve the imaging effect. In this paper, two different learned sparse transformations are used. The first covers more organizational information about the scanned object. The other can cover more noise artifacts. Both methods can improve the ability to learn sparse transformations to express various image information. Experimental results show that the algorithm is effective. [ABSTRACT FROM AUTHOR]

This study developed a device measuring the X-ray source-detector angle (SDA) and evaluated the imaging performance for diagnosing chest images. The SDA device consisted of Arduino, an accelerometer and gyro sensor, and a Bluetooth module. The SDA values were compared with the values of a digital angle meter. The performance of the portable digital radiography (PDR) was evaluated using the signal-to-noise (SNR), contrast-to-noise ratio (CNR), spatial resolution, distortion and entrance surface dose (ESD). According to different angle degrees, five anatomical landmarks were assessed using a five-point scale. The mean SNR and CNR were 182.47 and 141.43. The spatial resolution and ESD were 3.17 lp/mm (157 μm) and 0.266 mGy. The angle values of the SDA device were not significantly difference as compared to those of the digital angle meter. In chest imaging, the SNR and CNR values were not significantly different according to the different angle degrees. The visibility scores of the border of the heart, the fifth rib and the scapula showed significant differences according to different angles (p < 0.05), whereas the scores of the clavicle and first rib were not significant. It is noticeable that the increase in the SDA degree was consistent with the increases of the distortion and visibility score. The proposed PDR with a SDA device would be useful for application in the clinical radiography setting according to the standard radiography guidelines. [ABSTRACT FROM AUTHOR]

Flexible active matrix display technology has been adapted to create new flexible photo-sensing electronic devices, including flexible X-ray detectors. Monolithic integration of amorphous silicon (a-Si) PIN photodiodes on a flexible substrate poses significant challenges associated with the intrinsic film stress of amorphous silicon. This paper examines how altering device structuring and diode passivation layers can greatly improve the electrical performance and the mechanical reliability of the device, thereby eliminating one of the major weaknesses of a-Si PIN diodes in comparison to alternative photodetector technology, such as organic bulk heterojunction photodiodes and amorphous selenium. A dark current of 0.5 pA/mm² and photodiode quantum efficiency of 74% are possible with a pixelated diode structure with a silicon nitride/SU-8 bilayer passivation structure on a 20 μm-thick polyimide substrate. [ABSTRACT FROM AUTHOR]

Hospitals, especially their emergency services, receive a high number of wrist fracture cases. For correct diagnosis and proper treatment of these, images obtained from various medical equipment must be viewed by physicians, along with the patient's medical records and physical examination. The aim of this study is to perform fracture detection by use of deep-learning on wrist X-ray images to support physicians in the diagnosis of these fractures, particularly in the emergency services. Using SABL, RegNet, RetinaNet, PAA, Libra R-CNN, FSAF, Faster R-CNN, Dynamic R-CNN and DCN deep-learning-based object detection models with various backbones, 20 different fracture detection procedures were performed on Gazi University Hospital's dataset of wrist X-ray images. To further improve these procedures, five different ensemble models were developed and then used to reform an ensemble model to develop a unique detection model, 'wrist fracture detection-combo (WFD-C)'. From 26 different models for fracture detection, the highest detection result obtained was 0.8639 average precision (AP50) in the WFD-C model. Huawei Turkey R&D Center supports this study within the scope of the ongoing cooperation project coded 071813 between Gazi University, Huawei and Medskor. [ABSTRACT FROM AUTHOR]

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature. [ABSTRACT FROM AUTHOR]

Currently, the tuberculosis (TB) detection model based on chest X-ray images has the problem of excessive reliance on hardware computing resources, high equipment performance requirements, and being harder to deploy in low-cost personal computer and embedded devices. An efficient tuberculosis detection model is proposed to achieve accurate, efficient, and stable tuberculosis screening on devices with lower hardware levels. Due to the particularity of the chest X-ray images of TB patients, there are fewer labeled data, and the deep neural network model is difficult to fully train. We first analyzed the data distribution characteristics of two public TB datasets, and found that the two-stage tuberculosis identification (first divide, then classify) is insufficient. Secondly, according to the particularity of the detection image(s), the basic residual module was optimized and improved, and this is regarded as a crucial component of this article's network. Finally, an efficient attention mechanism was introduced, which was used to fuse the channel features. The network architecture was optimally designed and adjusted according to the correct and sufficient experimental content. In order to evaluate the performance of the network, it was compared with other lightweight networks under personal computer and Jetson Xavier embedded devices. The experimental results show that the recall rate and accuracy of the E-TBNet proposed in this paper are better than those of classic lightweight networks such as SqueezeNet and ShuffleNet, and it also has a shorter reasoning time. E-TBNet will be more advantageous to deploy on equipment with low levels of hardware. [ABSTRACT FROM AUTHOR]

Simulation results are presented that explore an innovative, new design for X-ray detection in the 20–50 keV range that is an alternative to traditional direct and indirect detection methods. Typical indirect detection using a scintillator must trade-off between absorption efficiency and spatial resolution. With a high-Z layer that down-converts incident photons on top of a silicon detector, this design has increased absorption efficiency without sacrificing spatial resolution. Simulation results elucidate the relationship between the thickness of each layer and the number of photoelectrons generated. Further, the physics behind the production of electron-hole pairs in the silicon layer is studied via a second model to shed more light on the detector's functionality. Together, the two models provide a greater understanding of this detector and reveal the potential of this novel form of X-ray detection. [ABSTRACT FROM AUTHOR]

Automatic defect detection of tire has become an essential issue in the tire industry. However, it is challenging to inspect the inner structure of tire by surface detection. Therefore, an X-ray image sensor is used for tire defect inspection. At present, detection of defective tires is inefficient because tire factories commonly conduct detection by manually checking X-ray images. With the development of deep learning, supervised learning has been introduced to replace human resources. However, in actual industrial scenes, defective samples are rare in comparison to defect-free samples. The quantity of defective samples is insufficient for supervised models to extract features and identify nonconforming products from qualified ones. To address these problems, we propose an unsupervised approach, using no labeled defect samples for training. Moreover, we introduce an augmented reconstruction method and a self-supervised training strategy. The approach is based on the idea of reconstruction. In the training phase, only defect-free samples are used for training the model and updating memory items in the memory module, so the reproduced images in the test phase are bound to resemble defect-free images. The reconstruction residual is utilized to detect defects. The introduction of self-supervised training strategy further strengthens the reconstruction residual to improve detection performance. The proposed method is experimentally proved to be effective. The Area Under Curve (AUC) on a tire X-ray dataset reaches 0.873, so the proposed method is promising for application. [ABSTRACT FROM AUTHOR]

Lab-based X-ray computed tomography (XCT) systems use X-ray sources that emit a polychromatic X-ray spectrum and detectors that do not detect all X-ray photons with the same efficiency. A consequence of using a polychromatic X-ray source is that beam hardening artefacts may be present in the reconstructed data, and the presence of such artefacts can degrade XCT image quality and affect quantitative analysis. If the product of the X-ray spectrum and the quantum detection efficiency (QDE) of the detector are known, alongside the material of the scanned object, then beam hardening artefacts can be corrected algorithmically. In this work, a method for estimating the product of the X-ray spectrum and the detector's QDE is offered. The method approximates the product of the X-ray spectrum and the QDE as a Bézier curve, which requires only eight fitting parameters to be estimated. It is shown experimentally and through simulation that Bézier curves can be used to accurately simulate polychromatic attenuation and hence be used to correct beam hardening artefacts. The proposed method is tested using measured attenuation data and then used to calculate a beam hardening correction for an aluminium workpiece; the beam hardening correction leads to an increase in the contrast-to-noise ratio of the XCT data by 41% and the removal of cupping artefacts. Deriving beam hardening corrections in this manner is more versatile than using conventional material-specific step wedges. [ABSTRACT FROM AUTHOR]

In this study, we investigated the characteristics of an organic-inorganic hybrid indirect-type X-ray detector with a CH3NH3PbI3 (MAPbI3) perovskite active layer. A layer with a thickness of 192 nm annealed at 100 °C showed higher absorption, higher crystallinity, and lower surface roughness than did perovskite layers made under different conditions. In the indirect X-ray detector, a scintillator coupled with the detector to convert X-ray photons to visible photons, and the converted photons were absorbed by the active layer to generate charge carriers. The detector with the optimized MAPbI3 (192 nm thick and 100 °C annealing condition) active layer was coupled with a CsI(Tl) scintillator which consisted of 400 μm thick CsI and 0.5 mm thick Al, and achieved the highest sensitivity, i.e., 2.84 mA/Gy·cm2. In addition, the highest short-circuit current density (JSC), i.e., 18.78 mA/cm2, and the highest mobility, i.e., 2.83 × 10−4 cm2/V·s, were obtained from the same detector without the CsI(Tl) scintillator. [ABSTRACT FROM AUTHOR]

This paper explores the prospect of CMOS devices to assay lead in drinking water, using calorimetry. Lead occurs together with traces of radioisotopes, e.g., 210 Pb , producing γ -emissions with energies ranging from 10 k e V to several 100 k e V when they decay; this range is detectable in silicon sensors. In this paper we test a CMOS camera (Oxford Instruments Neo 5.5) for its general performance as a detector of X-rays and low energy γ -rays and assess its sensitivity relative to the World Health Organization upper limit on lead in drinking water. Energies from 6 k e V to 60 k e V are examined. The CMOS camera has a linear energy response over this range and its energy resolution is for the most part slightly better than 2%. The Neo sCMOS is not sensitive to X-rays with energies below ∼ 10   k e V . The smallest detectable rate is 40 ± 3 m Hz , corresponding to an incident activity on the chip of 7 ± 4 Bq. The estimation of the incident activity sensitivity from the detected activity relies on geometric acceptance and the measured efficiency vs. energy. We report the efficiency measurement, which is 0.08(2)% (0.0011(2)%) at 26.3 k e V ( 59.5 k e V ). Taking calorimetric information into account we measure a minimal detectable rate of 4 ± 1 m Hz ( 1.5 ± 1 m Hz) for 26.3 k e V ( 59.5 k e V ) γ -rays, which corresponds to an incident activity of 1.0 ± 6 Bq ( 57 ± 33 Bq). Toy Monte Carlo and Geant4 simulations agree with these results. These results show this CMOS sensor is well-suited as a γ - and X-ray detector with sensitivity at the few to 100 ppb level for 210 Pb in a sample. [ABSTRACT FROM AUTHOR]

Silicon shows very high detection efficiency for low-energy photons, and the silicon pixel sensor provides high spatial resolution. Pixelated silicon sensors facilitate the direct detection of low-energy X-ray radiation. In this study, we developed junction field effect transistors (JFETs) that can be integrated into a pixelated silicon sensor to effectively handle many signal readout channels due to the pixelated structure without any change in the sensor resolution; this capability of the integrated system arises from the pixelated structure of the sensor. We focused on optimizing the JFET's switching function, and simulated JFETs with different fabrication parameters. Furthermore, prototype JFET switches were designed and fabricated on the basis of the simulated results. It is important not only to keep the low leakage currents in the JFET but also reduce the current flow as much as possible by providing a high resistance when the JFET switch is off. We determined the optimal fabrication conditions for the effective switching of the JFETs. In this paper, we present the results of the measurement of the switching capability of the fabricated JFETs for various design variables and fabrication conditions. [ABSTRACT FROM AUTHOR]

The effects of sub-band-gap light radiation on the performance of CdZnTe photon-counting X-ray detectors were studied using infrared light with different wavelengths in the region of 980–1550 nm. The performance of the detectors for X-ray detection was improved by the radiation of infrared light with the wavelengths of 1200 nm and 1300 nm. This was because the increase of the electron indirect transition, and the weakening of the built-in electric field induced by the trapped holes, reduced the drift time of the carrier, and increased the charge collection efficiency. To further analyze the intrinsic behavior of the trapped charge, the deep-level defects of CdZnTe crystal were measured by thermally stimulated current spectroscopy (TSC). The deep-level defect indicated by the trap named T4 in TSC spectra with the ionization energy of 0.43 eV should be responsible for the performance deterioration of CdZnTe detectors. [ABSTRACT FROM AUTHOR]

In this paper, we report on the development of a monolithic active pixel sensor for X-ray imaging using 0.2 µm fully depleted silicon-on-insulator (SOI)-based technology to support next generation astronomical satellite missions. Detail regarding low-noise dual-gain SOI based pixels with a charge sensitive amplifier and pinned depleted diode sensor structure is presented. The proposed multi-well sensor structure underneath the fully-depleted SOI allows the design of a detector with low node capacitance and high charge collection efficiency. Configurations for achieving very high charge-to-voltage conversion gain of 52 µV/e− and 187 µV/e− are demonstrated. Furthermore, in-pixel dual gain selection is used for low-noise and wide dynamic range X-ray energy detection. A technique to improve the noise performance by removing correlated system noise leads to an improvement in the spectroscopic performance of the measured X-ray energy. Taken together, the implemented chip has low dark current (44.8 pA/cm2 at −30 °C), improved noise performance (8.5 e− rms for high gain and 11.7 e− rms for low gain), and better energy resolution of 2.89% (171 eV FWHM) at 5.9 keV using 55Fe and 1.67% (234 eV FWHM) at 13.95 keV using 241Am. [ABSTRACT FROM AUTHOR]