Deep Defects Research Articles

Numerical sensitivity analysis of corroded pipes and burst pressure prediction using finite element modeling

Accurately predicting the failure pressure for corroded pipes transmitting natural resources such as oil and gas is crucial to prevent catastrophic failure and save costs. Several parameters affect the pipe's burst pressure, precisely the defective width, which is essential yet under debate. In this study, parameters such as the corrosion depth (25%, 50%, and 75%), angle (10⁰, 15⁰, 45⁰ and 90⁰), and length-to-diameter ratio “Lc/D" (0.25, 0.5, 1 and 1.5), were numerically investigated through sensitivity analysis, besides the pipes' size “D/t" ratio (16.19, 23.67, 42.64 and 79.79). The study also explored the adequacy of the previous burst pressure predicting models and proposed a new model through FE modeling. The results revealed that defect level significantly reduced the burst pressure regardless of defect angle, length, and pipe size. The defect angle ranges from 10⁰ to 45⁰, and the Lc/D ratio is less than 1, significantly impacting the burst pressure reduction for shallow and deep defects (25% and 75% wall loss). However, this impact disappears at the Lc/D ratio equals one. The findings showed that the old predictions, ASME B31G and RSTRENG, were more conservative than the newer models, DNV and Leis, for shallow defects and non-conservative for deep defects with intermediate lengths. The models that considered the material's strain hardening, such as Shaui, Leis, Ma, phan, and Netto, enhanced the predictions compared to the old ones, especially at extended corrosion. Finally, the design charts through the proposed model were generated for further adoption by codes.

Cracking failure analysis on a high-frequency electric resistance welding pipe in buried fire water pipeline

Leakage of a buried fire water pipeline occurred after in service of 15 years. It was manufactured from a Q235B carbon structural steel plate by high-frequency electric resistance welding. The water pressure increased from 0.8 MPa at a normal operating condition to 1.2 MPa at the tube burst moment. Cracks propagate along the weld and incomplete fusion defects are observed. This paper aims to explore the cracking failure through a combination method of experimental tests and theoretical analysis. The metallographic analysis clearly shows that the crack is located in the fusion zone and grains are coarse in that zone. Mechanical property tests indicate tensile properties of the anti-corrosion layer meet requirements. Finite element analysis reveals that the pipeline with an incomplete fusion can safely operate under a pressure of 0.8 MPa, while fracture occurs under the pressure of 1.2 ∼ 1.3 MPa. The results indicated that the deep unfused defect of the welded joint was the root cause of cracking, and selective seam weld corrosion also played an important role. The failure progress and mechanism were discussed in details.

Impact of Na diffusion on Cu(In, Ga)Se2 solar cells: Unveiling the role of active defects using thermal admittance spectroscopy

In this work, Cu(In, Ga)Se2 (CIGS) based thin-film solar cells have been treated with the alkali element via incorporating Mo:Na layer into the device structure. A 600 nm thick Mo:Na layer behaved as a source for Na diffusion throughout the CIGS layer. The improvements have resulted in reduced depletion barrier width from ∼0.55 to 0.25 µm and increased p-type conductivity that improved the overall device performance from 7.06 to 12.06% for CIGS/Mo/Mo:Na in comparison with CIGS/Mo based solar cells. Thermal admittance spectroscopy has been analyzed to unveil the consequence of Na on shallow/deep-level defects and correlate with the CIGS/Mo junction properties for both the CIGS solar cells. The defect distribution at the CIGS/buffer interface has been altered, the activation energy reduced from 260 meV to 63 meV assigned to deep level defect and 22 meV to 12 meV assigned shallow defects (VCu acceptor) that can be assigned to in the CIGS layer.

Deep Learning-Based Defect Detection Framework for Ultra High Resolution Images of Tunnels

This study proposes a defect detection framework to improve the performance of deep learning-based detection models for ultra-high resolution (UHR) images generated by tunnel inspection systems. Most of the scanning technologies used in tunnel inspection systems generate UHR images. Defects in real-world images, on the other hand, are noticeably smaller than the image. These characteristics make simple preprocessing applications, such as downscaling, difficult due to information loss. Additionally, when a deep learning model is trained by the UHR images under the limited computational resource for training, problems may occur, including a reduction in object detection rate, unstable training, etc. To address these problems, we propose a framework that includes preprocessing and postprocessing of UHR images related to image patches rather than focusing on deep learning models. Furthermore, it includes a method for supplementing problems according to the format of the data annotation in the preprocessing process. When the proposed framework was applied to the UHR images of a tunnel, the performance of the deep learning-based defect detection model was improved by approximately 77.19 percentage points (pp). Because the proposed framework is for general UHR images, it can effectively recognize damage to general structures other than tunnels. Thus, it is necessary to verify the applicability of the defect detection framework under various conditions in future works.

Critical Evaluation of the Photovoltaic Performance of (AgI)x(BiI3)y Thin Films from the Viewpoint of Ultrafast Spectroscopy and Photocurrent Experiments

Silver iodobismuthate thin films obtained from mixtures of AgI and BiI3 have been frequently suggested as promising alternatives for lead-based perovskite materials in photovoltaic applications. Here, we investigated (AgI)x(BiI3)y thin films with stoichiometric ratios (x/y) of 3:1, 2:1, 1:1, and 1:2 produced via a low-temperature (<100 °C) spin coating process from AgI/BiI3 solutions in dimethyl sulfoxide. Several critical observations on the basis of ultrafast broadband UV–vis transient absorption spectroscopy and photocurrent spectroscopy were made for these Ag–Bi–I materials, which will have a considerable impact on their photovoltaic performance: Their carrier recombination kinetics were independent of the initial carrier number density over the range of 1.3–6.6 × 1017 cm–3 and well approximated by a monoexponential decay with a rate constant krec in the range of 2.0–4.3 × 108 s–1, which is consistent with trap-mediated charge-carrier recombination. Moreover, pronounced coherent phonon dynamics was observed for all of these (AgI)x(BiI3)y compounds, thereby suggesting strong electron–phonon coupling, which will favor charge-carrier localization and nonradiative carrier recombination, in agreement with the virtually absent photoluminescence of these materials. In addition, Fourier-transform step-scan photocurrent spectroscopy (FTPS) on AgBi2I7 provided an Urbach energy of 70 meV and found indications for deep defects (0.6 eV below the band gap), which was also consistent with a trap-mediated recombination mechanism. A combination of all of these effects is likely responsible for the still quite low light-harvesting performance of this class of materials, which has been reported to show photovoltaic conversion efficiencies (PCEs) below 5%. Finally, for “AgI-rich” compounds (3:1, 2:1), we found a substantial separate contribution of carrier loss processes through ultrafast relaxation in AgI domains with time constants of 0.73 and 2.4 ps.

Non-destructive evaluation of the friction stir welding process, generalizing a deep neural defect detection network to identify internal weld defects across different aluminum alloys

Friction stir welding (FSW) is a solid-state welding process, which has significantly disrupted welding technology particularly for aluminum alloy applications. Due to its high-quality welds in all aluminum alloys, comparatively low heat input with high energy efficiency and ecological friendliness, FSW is used in a rapidly growing number of applications. Currently, destructive and non-destructive testing methods are attached as a separate process step to verify weld seam quality, detecting imperfections late in production and requiring costly rework or scrapping of the assembly. Various studies have shown the possibility of using deep neural networks (DNN) to evaluate weld quality and detect welding defects based on recorded data. In this study, conducted within the scope of RWTH Aachen’s Cluster of Excellence, Internet of Production, recurrent neural networks (RNN), and convolutional neural networks (CNN) were successfully trained to classify FSW force data sets, generated while joining different aluminum alloys over a wide range of welding parameters. For internal weld defects bigger than 0.08 mm, detection accuracies over 95% were achieved using bidirectional long short-term memory (BiLSTM) networks when limited to a single alloy and thickness. The classification accuracy dropped to ~ 90% when using multiple alloys and sheet thicknesses. The comparison between different network types’ classification accuracy as well as their ability to generalize the defect detection across different welding tasks with varying sheet thicknesses, respective welding tools, and different Al alloys is shown. The systems aim at offering a reliable and cost-efficient quality monitoring solution with a wide range of applicability, increasing the acceptance of the friction stir welding process as well as confidence in the resulting weld seam quality.

Impact of Ga and N Vacancies at the GaN m -Plane on the Carrier Dynamics of Micro-Light-Emitting Diodes

Micro-light-emitting diodes ($\text{\ensuremath{\mu}}\mathrm{LEDs}$) based on $\mathrm{Ga}\mathrm{N}$ are a key component for next-generation displays, but serious surface $e$-$h$ recombination deteriorates the device efficiency. To gain microscopic insights into the surface recombination, we use hybrid functional first-principles calculations to investigate $\mathrm{Ga}$ (${V}_{\mathrm{Ga}}$) and $\mathrm{N}$ (${V}_{\mathrm{N}}$) vacancies on the $\mathrm{Ga}\mathrm{N}$ $m$ plane that can be created considerably during the production of one-dimensional $\mathrm{Ga}\mathrm{N}$ structures. We find that the surface ${V}_{\mathrm{Ga}}$ is not critical for nonradiative Shockley-Read-Hall (SRH) recombination in light of the large formation energy (&gt;2 eV) and its shallow levels. In contrast, the surface ${V}_{\mathrm{N}}$ exhibits a low formation energy (2 eV) and develops a deep defect state near the midgap, indicating the possibility of becoming useful SRH recombination centers. By constructing configuration-coordinate diagrams, we demonstrate that the energy barriers for electron and hole capture on the surface ${V}_{\mathrm{N}}$ are small enough to cause significant capture coefficients due to strong electron-phonon coupling.

Excess PbBr2 Passivation of Large PbS Colloidal Quantum Dots to Reduce Dark-Current Density for Near-Infrared Detection

Small-size $\mathrm{Pb}\mathrm{S}$ colloidal quantum dots (CQDs) have achieved excellent performance in photoelectric conversion devices through the ligand-exchange method of mixed lead-halide passivation. However, with the increase of $\mathrm{Pb}\mathrm{S}$ CQD diameter, the proportion of (100) facets on the CQD surface increases and the original proportion of mixed lead-halide ligand cannot passivate (100) facets completely, which will introduce deep defects and deteriorate device performance. Here, we demonstrate an excessive ${\mathrm{Pb}\mathrm{Br}}_{2}$ concentration ligand strategy to sufficiently passivate large-size $\mathrm{Pb}\mathrm{S}$ CQDs with an absorption peak at 1300 nm. The first-principles calculation results suggest that ${\mathrm{Br}}^{\ensuremath{-}}$ can passivate (100) facets more efficiently compared with ${\mathrm{I}}^{\ensuremath{-}}$. With the increase of ${\mathrm{Pb}\mathrm{Br}}_{2}$ concentration (0--0.464 mmol/mL), both optical and electrical measurements imply that defects are effectively passivated, while carrier lifetime increases and dark-current density decreases. Finally, a device with specific detectivity of $5.22\ifmmode\times\else\texttimes\fi{}{10}^{12}$ Jones is obtained. This passivation strategy can also be used in other large-size $\mathrm{Pb}\mathrm{S}$ CQDs (diameter &gt;4 nm) to realize a better device performance.

Effect of pre-irradiation defects on helium trapping and diffusion in RAFM steel

In order to analyze the effect of pre-irradiation defects on helium trapping and diffusion behaviors in detail by slow positron beam, Si ion with high energies (300 keV and 6 MeV) and 100 eV helium plasma were sequentially introduced into reduced activation ferritic/martensitic steel. Massive microscopic defects were observed in Si ion irradiated specimens. Low energy helium (∼100 eV) atoms could diffuse into much deeper region in metals without displacement damage. In the diffusion process of helium atoms, they would be trapped by the Si-ion pre-irradiation defects. The formation of He-vacancy complexes would affect the annihilation of positrons with the electrons in pre-irradiation defects and decrease the S parameters. The shallow region defects induced by 300 keV irradiation have more obvious advantage of hindering the diffusion of helium over the deep defects than that in 6 MeV irradiation. Thus, the pre-introduced defects can trap effectively helium atoms and hinder their diffusion.

Calcium Sulfate Alone or Associated with Chorion Membrane in Deep Narrow Periodontal Defect? A Clinical and Radiographic Study

Abstract Background: Soft-tissue intervention over bony defects may impede periodontal regeneration, the sole purpose of periodontal therapy. Human chorion membrane (CM) enhances guided tissue regeneration (GTR) and restores lost tissue. Consequently, calcium sulfate may represent a promising class of bone graft substitutes that helps in significant attachment gain without GTR membrane in deep narrow defects. Hence, this study was conducted to evaluate the efficacy of calcium sulfate graft in periodontal regeneration and to weigh the rationale of using CM for GTR in deep narrow defects. Methods: Thirteen patients with bilateral deep narrow defects clinically and radiographically (26 sites, ≥7 mm pocket depth [PD]) were selected and randomly allocated to Group I (open flap debridement and calcium sulfate placement) and Group II (open flap debridement and calcium sulfate with CM placement). Clinical parameters such as PD, clinical attachment level, and radiographical intrabony defect were recorded at baseline and 6 months postsurgery. Results: Significant attachment gain was found in both groups with nonsignificant intergroup differences. Conclusion: Calcium sulfate with or without CM is able to restore deep narrow bony defects efficiently. Deep narrow defects itself able to prevent soft-tissue collapse and ascertain excellent periodontal regeneration.

Deep defects limiting the conversion efficiency of Sb2Se3 thin-film solar cells.

Quasi-one-dimensional (Q1D) semiconductor antimony selenide (Sb2Se3) shows great potential in the photovoltaic field, but the photoelectric conversion efficiency (PCE) of Sb2Se3-based solar cells has shown no obvious breakthrough during the past several years, of which the intrinsic reasons are pending experimentally. Here, we prepare high-quality Q1D Sb2Se3 thin films via the vapor transport deposition technique. By investigating the bandedge electronic level structure and carrier relaxation/recombination dynamics, we find that (i) the optimized Se-rich growth conditions can highly improve the crystal quality of the Q1D Sb2Se3 thin films, the carrier lifetime of which is substantially increased up to ∼8.3 μs; (ii) the Se-rich growth conditions have advantages to annihilate the deep selenium vacancies VSei (i = 1 and 3 for non-equivalent Se atomic sites) but is not effective for the deep donor VSe2, which locates at ∼0.3 eV (300 K) below the conduction band and intrinsically limits the PCE value of devices below ∼7.63%. This work suggests that further optimizing the Se-rich conditions to technically eliminate this kind of deep defect is still essential for preparing high-performance Sb2Se3 film solar cells.

Structural Defect Detection Technology of Transmission Line Damper Based on UAV Image

Overhead transmission lines suffer from extended exposure to harsh weather conditions. Metal dampers, a crucial protective fitting in the line, can effectively suppress the conductor’s vibration energy and prevent Aeolian vibration and ice shedding. To ensure the safety of operation of the damper, we are proposing a detection method for structural defect damper based on spatial relationship. First, the unmanned aerial vehicle (UAV) aerial damper images are processed with relative total variation (RTV) transform to obtain an enhanced image with a smooth texture and prominent foreground main structure. Second, the enhanced image is corrected by rotation so that the conductor remains horizontal. Next, based on the endpoint coordinates of the conductor, a foreground preselection box for improved GrabCut segmentation is automatically generated to extract the object dampers. Finally, the spatial relationship between the damper components in the segmentation results is regarded as the motive force of the damper structural defect diagnosis model to detect damage, inversion, slight, and serious deformation defects in sequence. We analyzed the performance of the proposed method through actual field tests, and the results demonstrated that the identification accuracy of the method is 95.76% when applied to a small sample set, which is higher than other existing methods based on traditional image techniques and deep learning defect detection, and can effectively identify different structural defects of dampers and provide reliable data for transmission line condition monitoring.

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

Study on characteristics of neutron-induced leakage current increase for SiC power devices

In this paper, the displacement damage degradation characteristics of silicon carbide (SiC) Schottky barrier diode (SBD) and metal oxide semiconductor field effect transistor (MOSFET) are studied under 14-MeV neutron irradiation. The experimental results show that the neutron irradiation with a total fluence of 1.18×10&lt;sup&gt;11&lt;/sup&gt; cm&lt;sup&gt;–2&lt;/sup&gt; will not cause notable degradation of the forward &lt;i&gt;I-V&lt;/i&gt; characteristics of the diode, but will lead to a significant increase in the reverse leakage current. A defect with energy level position of &lt;inline-formula&gt;&lt;tex-math id="Z-20230916130504"&gt;\begin{document}$ E_{\rm{C}} - 1.034 $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="18-20230976_Z-20230916130504.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="18-20230976_Z-20230916130504.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; eV is observed after irradiation by deep level transient spectroscopy (DLTS) testing, which is corresponding to the neutron-induced defect cluster in SiC. This deep level defect may cause the Fermi level of n-type doping drift region to move toward the mid-gap level. It ultimately results in the decrease of the Schottky barrier and the increase of the reverse leakage current. In addition, neutron-induced gate leakage increase is also observed for SiC MOSFET. The gate current corresponding to &lt;i&gt;V&lt;/i&gt;&lt;sub&gt;gs&lt;/sub&gt; = 15 V after irradiation increases nearly 3.3 times that before irradiation. The donor-type defects introduced by neutron irradiation in the oxide layer result in the difference in the conductivity mechanism of gate oxygen between before and after irradiation. The defects have an auxiliary effect on carrier crossing the gate oxide barrier, which leads to the increase of gate leakage current. The defects introduced by neutron irradiation are neutral after capturing electrons. The trapped electrons can cross a lower potential well and enter the conduction band to participate in conduction when the gate is positively biased, thus causing the gate current to increase with the electric field increasing. After electrons captured by donor-type defects enter the conduction band, positively charged defects are left from the gate oxide, leading to the negative shift of the transfer characteristics of SiC MOSFET. The results of DLTS testing indicate that the neutron irradiation can not only cause the intrinsic defect state of SiC material to change near the channel of MOSFET, but also give rise to new silicon vacancy defects. However, these defects are not the main cause of device performance degradation due to their low density relative to the intrinsic defect’s.

Effects of defects on electrical transport properties of anatase TiO&lt;sub&gt;2&lt;/sub&gt; polycrystalline under high pressure: AC impedance measurement

The electrical transport properties of anatase TiO&lt;sub&gt;2&lt;/sub&gt; polycrystalline have been systematically investigated by using high pressure &lt;i&gt;in-situ&lt;/i&gt; impedance spectroscopy measurements. The anomalous behaviors of resistance, parameter factor and relaxation frequency of grain and grain boundary can be found at 6.4, 11.5 and 24.6 GPa. The results indicate that the first two discontinuous points (6.4 and 11.5 GPa) correspond to the phase transitions of TiO&lt;sub&gt;2&lt;/sub&gt; from anatase to α-PbO&lt;sub&gt;2&lt;/sub&gt; and then to baddeleyite, respectively. Above 24.6 GPa, TiO&lt;sub&gt;2&lt;/sub&gt; completely transforms into the baddeleyite phase. Based on the change of grain resistance and grain boundary resistance under pressure, intrinsic defects play a crucial effect in the electrical transport properties of TiO&lt;sub&gt;2&lt;/sub&gt; at high pressures. At 6.4 GPa, the occurrence of phase transition gives rise to the variation of defects’ role, from a deep energy level defect (as a recombination centre) changes into a shallow energy level defect (providing carriers for the conduction and valence bands). In addition, the position of defect in energy band changes with pressure increasing. The phase transition of TiO&lt;sub&gt;2&lt;/sub&gt; at 6.4 GPa is the rearrangement of TiO&lt;sub&gt;6&lt;/sub&gt; octahedron, while the other one at 11.5 GPa can be attributed to the migration of oxygen Schottky defects from inner to surface. Combining the packing factor and relaxation frequency, the electrical transport properties of TiO&lt;sub&gt;2&lt;/sub&gt; under pressure are revealed, the packing factor and the relaxation frequency are closely related to the mobility and the carrier concentration, respectively. The activation energy of grain and grain boundary decrease with the pressure elevating, indicating that the transport of carriers in grain and grain boundary become easier under pressure, and the former is smoother than the latter owing to the activation energy of grain being smaller than that of grain boundary in the same pressure range. Moreover, the relaxation frequency ratio of TiO&lt;sub&gt;2&lt;/sub&gt; grain and TiO&lt;sub&gt;2&lt;/sub&gt; grain boundary decreases with pressure increasing, and the grain boundary effect under high pressure is not obvious.

Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance

Monitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively.

Efficiency enhancement of CZTSe solar cells based on in situ K-doped precursor

High FF: in situ K-doped precursors make large-grain and void-free absorbers. The doping-enhanced Sn diffusion eliminates the local content vibration, suppresses deep level defects, and to higher cell efficiency with greatly improved FF.

A single-stage bilayered skin reconstruction using Glyaderm® as an acellular dermal regeneration template results in improved scar quality: an intra-individual randomized controlled trial

Absence of almost the entire reticular dermal layer is inherent to the use of autologous split-thickness skin grafting (STSG) to close full-thickness wounds, often resulting in hypertrophic scars and contractures. Many dermal substitutes have been developed, but unfortunately most have varying results in terms of cosmetic and/or functional improvement as well as patient satisfaction, in addition to high costs. Bilayered skin reconstruction using the human-derived glycerolized acellular dermis (Glyaderm®) has been reported to result in significantly improved scar quality using a two-step procedure. Unlike the necessary two-step procedure for most commercially available dermal substitutes, in this study we aimed to investigate the use of Glyaderm® in a more cost-effective single-stage engrafting. This is a method which, if autografts are available, is preferred by the majority of surgeons given the reduction in costs, hospitalization time and infection rate. A prospective, randomized, controlled, intra-individual, single-blinded study was performed, investigating the simultaneous application of Glyaderm® and STSG vs. STSG alone in full-thickness burns or comparable deep skin defects. During the acute phase, bacterial load, graft take and time to wound closure were assessed and were the primary outcomes. Aesthetic and functional results (secondary outcomes) were evaluated at 3, 6, 9 and 12months follow-up using subjective and objective scar measurement tools. Biopsies for histological analysis were taken at 3 and 12months. A total of 66 patients representing 82 wound comparisons were included. Graft take (>95%), pain management and healing time were comparable in both groups. At 1year follow-up, the overall Patient and Observer Scar Assessment Scale assessed by the patient was significantly in favour of sites where Glyaderm® was used. Not infrequently, patients attributed this difference to improved skin sensation. Histological analysis showed the presence of a well-formed neodermis, with donor elastin present for up to 12months. A single-stage bilayered reconstruction with Glyaderm® and STSG results in optimal graft take without loss of Glyaderm® nor the overlaying autografts due to infection. The presence of elastin in the neodermis was demonstrated during long-term follow-up in all but one patient, which is a crucial factor contributing to the significantly improved overall scar quality as evaluated by the blinded patients. The trial was registered on clinicaltrials.gov and received the following registration code: NCT01033604.

Intrinsic defects at the interface of the FAPbI3/MAPbI3 superlattice: insight from first-principles calculations.

The use of a superlattice structure is an effective strategy to develop novel perovskites and obtain excellent light-absorbing materials. Based on first-principles calculations, we systematically studied the properties of intrinsic point defects at the interface of the FAPbI3/MAPbI3 superlattice. Our calculations show that charged defects are easier to form as compared to neutral ones at the superlattice interface due to low formation energies. Most defects with low formation energies have a shallow level in the band gap, and some deep level defects have high formation energies, so the superlattice perovskite exhibits high defect tolerance. PbI3+ is a dominant and detrimental defect, which acts as a non-radiative recombination center because it has low formation energy and a deep transition level. To avoid the generation of PbI3+ defects, it is suggested to synthesize FAPbI3/MAPbI3 superlattices under I-rich conditions. The calculated light absorption coefficients and photovoltaic performance parameters demonstrate that the presence of defects leads to a certain degree of reduction in light absorption and power conversion efficiency (PCE) of solar cells made of FAPbI3/MAPbI3 superlattices, but the excellent performance of the perovskite solar cell (PSC) is basically retained. The superlattice perovskites are still promising candidates for light-absorbing materials of PSCs. This study is expected to contribute to a better understanding of the properties of defects at the superlattice interface and provide theoretical support for the design of high performance PSCs.

Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey

Printed circuit boards (PCBs) are a nearly ubiquitous component of every kind of electronic device. With the rapid development of integrated circuit and semiconductor technology, the size of a PCB can shrink down to a very tiny dimension. Therefore, high-precision and rapid defect detection in PCBs needs to be achieved. This paper reviews various defect detection methods in PCBs by analysing more than 100 related articles from 1990 to 2022. The methodology of how to prepare this overview of the PCB defect detection methods is firstly introduced. Secondly, manual defect detection methods are reviewed briefly. Then, traditional image processing-based, machine learning-based and deep learning-based defect detection methods are discussed in detail. Their algorithms, procedures, performances, advantages and limitations are explained and compared. The additional reviews of this paper are believed to provide more insightful viewpoints, which would help researchers understand current research trends and perform future work related to defect detection.