Spatial Distribution Of Defects Research Articles

Predictive model of spatial nematic order in confined cell populations

Two-dimensional tissues made of spindle-shaped cells have many applications in the fields of tissue engineering and biotechnology. The uniformity of the tissues is critically affected by topological defects, which are singular points of cell alignment. For systematic control and analysis of defect distributions, this paper proposes a numerical method to predict and quantify spatial distributions of defects in two-dimensional domains. In the proposed method, spindle-shaped cells are modeled as nematic liquid crystals, whose alignment and Frank elastic energy are explicitly expressed. The equilibrium distributions of the defects can then be calculated using a Markov chain Monte Carlo method. The proposed method was experimentally verified by culturing mouse myoblast (C2C12) cells on microwells. The order of the defect scattering was almost the same as for the proposed estimation method, indicating that the proposed method can be used for the systematic design of topographical guides for controlling defect distributions.

Characterization Study of Deep-Level Defect Spatial Distribution, Emission Mechanisms, and Structural Identification

Abstract The characterization of defects in semiconductor materials and devices is crucial for enhancing the performance and reliability of semiconductor products. This tutorial review focuses on Deep Level Transient Spectroscopy (DLTS) as the primary analytical tool, thoroughly discussing its distinct advantages in deep-level defect characterization. However, it is unable to reveal the concentration-depth distribution of deep-level defects, neglects the dependency of carrier emission rates on the electric field, and fails to accurately identify defect structures.
To overcome these limitations, two enhanced DLTS techniques have been developed to extend the capabilities of DLTS. These enhancements include the utilization of graded fill pulse technology to accurately map defect distributions at various depths within devices, facilitating individual defect characterization across different layers of multilayered structures; the application of varying electric field strengths to samples to delve into the intricate physical mechanisms of defects during carrier emission processes; and the adjustment of the duration of electric pulse injection to monitor signal growth trends, deducing the microstructure of defects. The paper integrates research findings from a wide array of field experts, meticulously outlines a description of how to obtain the depth distribution of defect concentration in devices, furnishes quantitative criteria for both the Poole-Frenkel effect and phonon-assisted tunneling mechanisms of carrier emission, and provides specific examples for distinguishing between interface states/bulk defects and point defects/extended defects. This enhances both the theoretical and practical knowledge in this field. The advanced DLTS techniques outlined provide crucial guidance for defect characterization and performance optimization in semiconductor devices with new structures and materials.

Three-dimensional quantitative characterization of defects in inconel 625 superalloy based on deep learning image identification

Three-dimensional (3D) quantitative characterization of defects in superalloys is an important way to promote the ability of material design and service life prediction. In this work, 3D spatial distribution of defects for Inconel 625 superalloy manufactured by laser additive manufacturing (LAM) is carried out deep learning (DL) image identification technology and 3D image reconstruction. Firstly, computer tomography (CT) technology was used to obtain continuous slice images of sample. The U-net DL algorithm was applied to intelligently identify material defects in the continuous slices. On this basis, quantitative identification and analysis of spatial defect positions and typical sizes is achieved by using 3D reconstruction software. Compared with traditional threshold segmentation (TS) techniques, the defect recognition rate has significantly improved from 61.90 ​% to 95.00 ​%. This work provides a promising characterization method for efficient characterizing alloy defects and damage especially during material performance evaluation.

Eliminating Resistance-Capacitance Coupling Shielding for Depicting the Defect Landscape in Perovskite Solar Cells by Capacitance Spectroscopy.

Capacitance spectroscopy techniques have been widely utilized to evaluate the defect properties in perovskites, which contribute to the efficiency and operation stability development for perovskite solar cells (PSCs). Yet the interplay between the charge transporting layer (CTL) and the perovskite on the capacitance spectroscopy results is still unclear. Here, they show that a pseudo-trap-state capacitance signal is generated in thermal admittance spectroscopy (TAS) due to the enhanced resistance capacitance (RC) coupling caused by the carrier freeze-out of the CTL in PSCs, which could be discerned from the actual defect-induced trap state capacitance signal by tuning the series resistance of PSCs. By eliminating the RC coupling shielding effect on the defect-induced capacitance spectroscopy, it is obtain the actual defect density which is 4-folds lower than the pseudo-trap density, and the spatial distribution of defects in PSCs which reveals that the commonly adopted interface passivators can passivate the defects about 60 nm away from the decorated surface. It is further revealed that phenethylammonium ions (PEA+) possess a better passivation capability over octylammonium ions (OA+) due to the deeper passivation depth for PEA+ on the surface defects in perovskite films.

Defect-Mediated Faceted Lithium Nucleation on Carbon Composite Substrates.

The formation of uniform, nondendritic seeds is essential to realizing dense lithium (Li) metal anodes and long-life batteries. Here, we discover that faceted Li seeds with a hexagonal shape can be uniformly grown on carbon-polymer composite films. Our investigation reveals the critical role of carbon defects in serving as the nucleation sites for their formation. Tuning the density and spatial distribution of defects enables the optimization of conditions for faceted seed growth. Raman spectral results confirm that lithium nucleation indeed starts at the defect sites. The uniformly distributed crystalline seeds facilitate low-porosity Li deposition, effectively reducing Li pulverization during cycling and unlocking the fast-charging ability of Li metal batteries. At a 1 C rate, full cells using LiNi0.8Mn0.1Co0.1O2 cathode (4.5 mA h cm-2) paired with a lithium anode grown on carbon composite films achieve a 313% improvement in cycle life compared to baseline cells. Polymer composites with carbonaceous materials rich in defects are scalable, low-cost substrates for high-rate, high-energy-density batteries.

Flexural properties and fatigue limit of 3D-printed and milled resin-based materials.

The purpose of this study was to evaluate the flexural strength (FS), flexural modulus (FM), and fatigue limit (FL) of 3D-printed resin-based polymers and composites and compare them to 3D-printed composites. A bar-shaped specimen (25×2×2mm) was CAD designed according to ISO 4049:2019, and 60 duplicates of the 3D model were nested at a 45-degree angle with the printing platform and 3D-printed with three materials: denture teeth resin (Denture Teeth, Formlabs), temporary crown and bridge resin (Temporary CB, Formlabs), and composite (Flexcera Smile Ultra+, Desktop Health). The 3D model was also imported into a dental CAM software, duplicated 60 times, nested, and milled from a 3D-milled composite puck (Ivotion Denture Teeth, Ivoclar). All specimens were post-processed following the manufacturer's recommendation. The specimens were then subjected to a three-point bending test until failure using a Universal Testing Machine at a crosshead speed of 0.75mm/min, and FS and FM were calculated. The remaining thirty specimens were tested for Fatigue Limit using the staircase approach starting at 50% FS maximum up to 1.2M cycles at 10Hz. The data were analyzed using one-way ANOVA and the Weibull distribution (α = 0.05). The results showed that Ivotion and Flexcera had higher FS (110.3 ± 7.1MPa and 107.6 ± 6.4MPa, respectively) and FM (3.3 ± 0.1GPa and 3.0 ± 0.2GPa, respectively) compared to the 3D-printed Denture Teeth (FS = 66.4 ± 18.5MPa and FM = 1.8 ± 0.1GPa) and Temporary CB (FS = 79.6 ± 12.1MPa and FM = 2.7 ± 0.4GPa). Weibull analysis showed that the Ivotion and Flexcera had a more uniform and narrower spatial distribution of defects (m: 27.98 and 29.19) than the printed materials, which had m values of 8.17 and 4.11 for Temporary CB and Denture Teeth, respectively. Although no differences were found in the static properties (FS and FM) between Ivotion and Flexcera, Ivotion presented a higher endurance limit than Flexcera (51.43 vs. 40.95MPa). The Temporary CB presented 21.08MPa and Denture Teeth presented 17.80MPa of endurance limit. 3D-milled (Ivotion Denture Teeth) and 3D-printed (Flexcera Smile Ultra+) composites outperformed 3D-printed resins (Formlabs Denture Teeth and Temporary Crown & Bridge) in terms of flexural properties and fatigue resistance. 3D-milled (Ivotion) and 3D-printed (Flexcera) composites exhibited similar flexural properties, but 3D-milled composites showed a 25% higher fatigue endurance limit, suggesting improved clinical longevity.

Cascaded adaptive global localisation network for steel defect detection

Defect detection is crucial in ensuring the quality of steel products. This paper proposes a novel deep neural network, cascaded adaptive global location network (CAGLNet), for detecting steel surface defects. The main objective of this study is to address the challenges associated with the irregular shape and dense spatial distribution of defects on steel. To achieve this goal, CAGLNet integrates a feature extraction network that combines residual and feature pyramid networks, a cascade adaptive tree-structure region proposal network (CAT-RPN) that eliminates the need for prior knowledge, and a global localisation regression for steel defect detection. This paper evaluates the effectiveness of CAGLNet on the NEU-DET dataset and demonstrates that the proposed model achieves an average accuracy of 85.40% with a fast frames per second of 10.06, outperforming those state-of-the-art methods. These results suggest that CAGLNet has the potential to significantly improve the effectiveness of defect detection in industrial production processes, leading to increased production yield and cost savings. Abbreviations: AT-RPN, adaptive tree-structure region proposal network; CAGLNet, cascaded adaptive global location network; CAT-RPN, cascade adaptive tree-structure region proposal network; CNN, convolutional neural network; DNN, deep neural network; EPNet, edge proposal network; FPN, feature pyramid network; FCOS, fully convolutional one-stage detector; FPS, frames per second; GMM, Gaussian mixture model; IoU, intersection-over-union; ROIAlign, region of interest align; RPN, region proposal network; ResNet, residual network; ResNet50_FPN, residual network and feature pyramid network; SABL, side aware boundary localisation; SSD, single-shot multiBox detector; TPE, Tree-structured Parzen estimator

Neutron phase imaging by a Talbot-Lau interferometer at Kyoto University Reactor.

We have developed a new neutron phase imaging system with a Talbot-Lau interferometer for utilization at the CN-3 port of the Kyoto University Reactor. To achieve efficient differential-phase imaging and visibility (dark-field) imaging at this beamline, we adopted a relatively shorter design wavelength of 2.7 Å. By fabricating neutron absorption gratings with thick gadolinium absorbers, we were able to obtain clear moiré fringes with a high visibility of 55% for thermal neutrons. As a demonstration of its imaging capabilities for expanded actual utilization in the medium-sized sources, we observed additively manufactured rods of Inconel 718. Using visibility imaging, we successfully examined variations in the size of defects in the rods caused by hot isostatic pressing process. In addition, we conducted tomography measurements of the rods, which allowed us to reveal the spatial distribution of defects at sub-micrometer scales.

Probability distributions of mineral dissolution rates: the role of lattice defects

The correct quantification of mineral dissolution rates is a critical task for macroscopic reactive transport modeling. Previous studies showed a substantial rate variability of about two orders of magnitude, which cannot be explained by variance of external environmental parameters alone. If the rate cannot be predicted as a constant parameter, then the critical question is whether it can be predicted as a stable reproducible probability distribution. Although a large variety of factors may contribute to the overall variance across the scales, the effect of defect density and defect spatial distribution can be considered as one of the key variance sources. Here, we tested the reproducibility of probability distributions for Kossel crystals with a different amount and spatial configurations of lattice dislocations. We ran several tests on systems with the same configurations and calculated the probabilities of material flux. Surprisingly, we discovered that the density of dislocations has minimal impact on the probability distributions. However, the spatial location of dislocations has a substantial influence on the rate distributions reproducibility. In cases where multiple etch pits operate simultaneously, reproducible rate distributions are found regardless of the number of dislocations. In cases where dislocations formed clusters, one large etch pit controlled the entire surface, and sets of reproducible probability distributions were detected. Then, more complex statistical behavior is expected, since the result is path-dependent. These results have serious consequences for the implementation of rate distributions in reactive transport models. Further studies, however, are needed to provide clear guidance on relating surface morphologies, dislocation distributions, and dissolution rate variance. The role of material-specific properties, such as crystallographic structure and bonding, in rate distributions, should be additionally addressed. The role of grain boundaries, crystal size and crystal habit, including nanoparticulate forms, in rate variance, also should be addressed for practical applications.

Novel continuous fiber bending experiment to determine size- and spatial-distribution of surface defects in glass fibers

Tensile strength models for unidirectional composites require scaling of fiber strength distributions to small length scales in the range of the fiber radius to the ineffective length (5–200 μm). No test methods currently exist to measure and validate fiber strength at these length scales. In this paper, a novel continuous fiber bending test method is developed to measure the size and spatial distribution of defects in S-glass fibers. For this test method, a single glass fiber was placed between Kapton film and tape, and the sample is subjected to a sequence of controlled radii of curvatures ranging from 350 μm to 25 μm in a confocal microscope. From the bending tests, the number of defects and spacings between the defects over 780 mm length of fiber are obtained for each level of flexural stress. The Kapton film confines the fiber fragments and maintains fiber alignment allowing the same sample to be tested at lower radius of curvature (higher flexural strain/stress). The associated defect size is calculated using fracture mechanics. These results are used to map the size- and spatial-distribution of critical defects on the tension surface of a glass fiber. A methodology is presented to map additional defects around the circumference of the fiber, while maintaining the size and spatial distribution obtained from the bending tests. The results show that the lognormal distribution best fits the spatial distribution of defects along the fiber axis. The results also indicate that extrapolating the Weibull strength distribution (be it bimodal or unimodal) to gage lengths less than 200 μm leads to serious errors in fiber strength prediction.

Effects of Surface, Interface, and Defect on Zeolite Catalysis Probed by a 3D Anisotropic Model

Zeolite catalysts are characterized by complex surface, interface, and defect structures, which are tightly related to diffusion and subsequent reaction in these catalysts. Herein, we develop a three-dimensional anisotropic model to describe anisotropic diffusion, surface/interface barriers, and reactions in zeolites with rich defects. Benzene alkylation with ethylene catalyzed by ZSM-5 zeolite is taken as the model reaction system. The results show that diffusion anisotropy largely affects the apparent reaction rate of a zeolite catalyst, indicating that diffusion anisotropy should be accounted for. Surface and interface diffusion barriers play an important role when diffusion limitation is strong in zeolites, and it is necessary to regulate surface and interface structures in these cases. Introducing defects into zeolites is favorable. The optimal size, volume, and spatial distribution of defects are obtained to balance diffusion and reaction. This work provides a powerful model and some useful guidance for the optimal design of zeolite catalysts.

Disks of Oxygen Vacancies on the Surface of TiO2 Nanoparticles

Oxide nanopowders are widely used in engineering, and their properties are largely controlled by the defect structure of nanoparticles. Experimental data on the spatial distribution of defects in oxide nanoparticles are unavailable in the literature, and in the work presented, to gain such information, methods of nuclear reactions and deuterium probes were employed. The object of study was oxygen-deficient defects in TiO2 nanoparticles. Nanopowders were synthesized by the sol–gel method and laser evaporation of ceramic targets. To modify the defect structure in nanoparticles, nanopowders were subjected to vacuum annealings. It was established that in TiO2 nanoparticles there form two-dimensional defects consisting of six titanium atoms that occupy the nanoparticle surface and result in a remarkable deviation of the chemical composition from the stoichiometry. The presence of such defects was observed in two cases: in TiO2 nanoparticles alloyed with cobalt, which were synthesized by the sol–gel method, and in nonalloyed TiO2 nanoparticles synthesized by laser evaporation of ceramic target. The concentration of the defects under study can be varied in wide limits via vacuum annealings of nanopowders which can provide formation on the surface of oxide nanoparticles of a solid film of titanium atoms 1–2 monolayers in thickness.

Predicting the effects of plasma-induced damage on p–n junction leakage and its application in the characterization of defect distribution

Understanding the effects of defect creation during plasma exposure is crucial for designing future ultra-low leakage current devices. Created defects play a role as carrier conduction paths, leading to an increase in the p–n junction leakage current (ΔJpn). Herein, we propose a model focusing on the effects of created defects on ΔJpn in combination with technology computer-aided design simulations. Three different defect spatial distributions, ndam(x), (linear, exponential, and Gaussian) were implemented for predicting ΔJpn under various operating conditions. It was confirmed that ΔJpn is strongly dependent on ndam(x), in addition to the total number of defects and generally accepted energy levels. The prediction model was implemented to experimentally assign the profile of defects created by an emerging mechanism—lateral straggling of incident ions—under fluorocarbon-containing plasma exposure, which is commonly employed in electrical contact opening steps. Devices with various lateral p–n junction widths and contact opening areas were exposed to the plasma. The experimental results of ΔJpn revealed that the profile of defects in the lateral direction was exponential. The proposed model prediction scheme is useful for designing plasma processes and circuit layouts to realize future ultra-low leakage current devices.

Statistical study of the size and spatial distribution of defects in a cast aluminium alloy for the low fatigue life assessment

Cast aluminium alloys, and more widely cast materials, are frequently used in industry. The casting process allows for complex geometries of parts, but, on the downside, often causes materials voids. It is well known these material defects are harmful for material fatigue performances, but the nature of these defects, in a statistical manner, are more seldom studied. This paper aims at proposing a methodology for finding the underlying characteristics of the defect population (size and spatial distribution) and determine their implication on fatigue behaviour in the presence of stress/strain gradients (notched specimens). To do so, various statistical tools are brought from different fields, such as point processes, and applied to experimentally observed defect distributions (by μCT tomography on virgin test specimens). The population of defects is clearly identified, and it is shown these defects are not randomly distributed, but rather in cluster. It is also shown there is no strong link between the defect size an it’s location. Knowing the statistics of the defect population, it is then possible to confront the result of fatigue tests (and the observed initiating defects) with the simulated defect population: the fatigue crack initiation mechanisms, which favour (sub-) surface rather than core initiating defects, reduce the size of the active zone and therefore artificially shift the defect size distribution (by reducing their number).

Measuring sub-surface spatially varying thermal conductivity of silicon implanted with krypton

The thermal properties of semiconductors following exposure to ion irradiation are of great interest for the cooling of electronic devices; however, gradients in composition and structure due to irradiation often make the measurement difficult. Furthermore, the nature of spatial variations in thermal resistances due to spatially varying ion irradiation damage is not well understood. In this work, we develop an advancement in the analysis of time-domain thermoreflectance to account for spatially varying thermal conductivity in a material resulting from a spatial distribution of defects. We then use this method to measure the near-surface (≲1 μm) thermal conductivity of silicon wafers irradiated with Kr+ ions, which has an approximate Gaussian distribution centered 260 nm into the sample. Our numerical analysis presented here allows for the spatial gradient of thermal conductivity to be extracted via what is fundamentally a volumetric measurement technique. We validate our findings via transmission electron microscopy, which is able to confirm the spatial variation of the sub-surface silicon structure, and provide additional insight into the local structure resulting from the effects of ion bombardment. Thermal measurements found the ion stopping region to have a nearly 50× reduction in thermal conductivity as compared to pristine silicon, while TEM showed the region was not fully amorphized. Our results suggest this drastic reduction in silicon thermal conductivity is primarily driven by structural defects in crystalline regions along with boundary scattering between amorphous and crystalline regions, with a negligible contribution being due to implanted krypton ions themselves.

Kinetic Model of Acid Gas Induced Defect Propagation in Zeolitic Imidazolate Frameworks

Understanding the degradation of nanoporous materials under exposure to common acid gas contaminants (e.g., SO2, CO2, NO2, and H2S) is essential to elongate their lifetime and thus enable their practical applications in separations and catalysis. Previous theoretical investigations have focused on the formation of isolated point defects, which are insufficient to provide direct insights into the long-term evolution of the bulk properties of materials such as zeolitic imidazolate frameworks (ZIFs) under sustained acid gas exposure. To bridge this divide in both length and time scales, we developed a first-principles lattice-based kinetic model to simulate the defect propagation and bulk material breakdown in ZIFs. This model closely reproduces the experimentally measured macroscopic evolution of the time-dependent bulk materials proprieties and also yields important new insights regarding the autocatalytic nature of ZIF degradation and the spatial distribution of defects. Our results suggest new experimental directions to identify nascent defect clusters in degraded ZIFs and avenues to mitigate degradation under challenging conditions of acid gas exposure.

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. We develop a rapid automatic computer vision pipeline (∼0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

Electric field dependence of thermal conductivity in bulk systems and nanosystems with charged mobile defects

In systems with charged mobile defects, application of an external electric field modifies the spatial distribution of defects. We obtain the corresponding dependence of the thermal conductivity on the electric field due to the redistribution of defects, both in bulk systems (heat transfer described by Fourier’s law) and in nanosystems (heat transfer described by phonon hydrodynamics). We consider the electric field transverse to the heat flux as well as the electric field parallel to the heat flux; in the latter case, we consider a superlattice composed of alternating layers with defects and without defects. For transverse electric fields, the effective thermal conductivity is increased in bulk systems and decreased in nanosystems. This dependence on the field would allow us to control heat transport in an easy and reversible way without modifying the material.

Relationship between Inclusions and Internal Defect Spatial Distribution in Large Forging Piece for Wind Power Generation Gear

Relationship between Inclusions and Internal Defect Spatial Distribution in Large Forging Piece for Wind Power Generation Gear

Individual cascade annealing in BCC tungsten: effects of size and spatial distributions of defects.

To investigate effects of size and spatial distributions of defects from primary damage to annealing of an individual cascade, molecular dynamics (MD) and object kinetic Monte Carlo (OKMC) are applied for simulating cascade generation and annealing. MD cascade simulations of tungsten are carried out with two typical embedded atom method potentials for cascade energies in the range from 0.1 to 100 keV at 300 K. The simulation results show that even though the number of survival defects varies slightly, these two potentials produce very different interstitial cluster (IC) size distribution and defect spatial distribution with cascade energies larger than 30 keV. Furthermore, OKMC is used to model individual cascade annealing. It demonstrates that larger-sized ICs and closely distributed SIAs in the cascade region will induce a much higher recombination fraction for individual cascade annealing. Therefore, special attention should be paid to the size and spatial distributions of defects for primary damage in the multi-scale simulation framework.