Actual Defect Research Articles

Development of an EUV Microscope with Focused Coherent EUV Based on Coherent Diffraction Imaging Method for Defect Evaluation on an EUV Mask

SUMMARYFor evaluation of defects on extreme ultraviolet (EUV) masks at the blank state of manufacturing, we developed a microcoherent EUV scatterometry microscope (micro‐CSM). The illumination source is coherent EUV light with a 140‐nm focus diameter on the defect using a Fresnel zoneplate. This system directly observes the reflection and diffraction signals from a phase defect. The phase and the intensity image of the defect is reconstructed with the diffraction images using ptychography, which is an algorithm of the coherent diffraction imaging. We observed programmed phase defect on a blank EUV mask. Phase distributions of these programmed defects were well reconstructed quantitatively. We also observed actual defects which were inspected by the ABI tool. Actual amplitude defect and phase defect images were reconstructed with intensity and phase contrast. The reconstructed image indicated the defect type of these defect. The micro‐CSM is very powerful tool to evaluate an EUV phase defect.

Reflection phenomena of SH plate waves at actual defects and defect depth estimations using AI

Reflection phenomena of SH plate waves at actual defects and defect depth estimations using AI

Sizing of Defect Using Magnetic Memory Signal Based on the Reconstruction Algorithm

A metal magnetic memory (MMM) testing technique is an extensively used qualitative method for locating the position of possible damages in ferromagnetic metal structures. The mechanism of the MMM technique is described by the magnetomechanical effect under the constant magnetizing field of the Earth. Discontinuous defect remarkably affects MMM signals. For the exploration of the influences of defect on MMM signals and the determination of a method to size the defect, experimentation and reconstruction calculation were performed in this paper. Stress was imported into a specimen fabricated with a circular defect to generate the stress-induced MMM signal. The defect size and the residual magnetic field of the specimen were measured after loading. The stress-induced magnetization inside the specimen was reconstructed with measured magnetic field signals using a reconstruction algorithm. The influences of the discontinuous defect on the stress-induced magnetization were quantitatively analyzed. A method for defect imaging and sizing using the MMM technique was proposed. Results corroborate that the reconstructed defect width is congruent with the actual defect width, thereby confirming the effectiveness and applicability of the proposed method.

The heat loss calculating methods of external walls in the buildings reconstruction

Heat loss analysis in cladding of brick buildings under reconstruction is presented. Thermograms obtained under thermovision inspection and window systems operating experience in conditions of the Republic of Bashkortostan are thoroughly studied. Live issue of increasing buildings energy performance in public utilities sector may largely be solved by replacement of existing window units made of wood to new PVC profile windows equipped with multi-glazed glass units both in brick and frame-panel old buildings. Significant heat loss occurs in junctions between the window frames and the wall in jamb areas. Therefore for the heat conduction matrix for the finite element is used the heat balance equation. Use of the software application to choose certain measures to eliminate the thermal bridges enables to get the thermotechnical calculations in the junction between the window and the exterior wall in the form of temperature fields. Practical recommendations for arrangement of heat insulation in junctions between the window frames and the wall to eliminate actual defects and for normal room conditions are made under examinations. To get the heat conduction matrix for the finite element we will use the heat balance equation.

Quantitative prediction of oxide inclusion defects inside the casting and on the walls during cast-filling processes

Oxide inclusions are a common defect in casting production, but their formation and evolution processes are difficult to directly observe experimentally. To accurately predict these, one should calculate the formation rate of the oxide inclusion as well as its wall adhesion. Here, we propose a formation rate model that is relevant to the volume fraction of liquid metal, the temperature, and the current oxide inclusion density. A boundary condition was developed to handle the adhesion and accumulation on the wall; thus, a quantitative indicator is available to directly obtain the content of the oxide inclusion defects on different parts. We used the open source computational fluid dynamics (CFD) software OpenFOAM to predict oxide inclusion defects inside the casting and on the walls during cast-filling processes. A representative aluminum alloy-casting technology was simulated to analyze the effects of different runner systems on the oxide inclusion distributions inside casting and on the wall. The simulation results largely coincide with experimental data from the literature. A practical copper alloy-casting technology was then calculated, and the simulation results of the oxide inclusion defects are consistent with the actual casting defect distribution. This confirms the utility of the model.

Numerical Validations of Flaw Shape Idealization Methods to Burst Pressure Estimations of Steam Generator Tube With Axial Surface Flaws

This work investigates the applicability of the flaw shape idealization methods to carry out the structural integrity assessment of steam generator (SG) tubes under internal pressure with complicated axial inner and outer surface flaws that were typically found during the in-service-inspection (ISI). In terms of flaw shape, three different shapes of flaws which can be detected during an actual ISI are considered, i.e., long symmetric flaw, asymmetric inclined flaw and narrow, symmetric deep flaw. As for flaw shape idealization methods for the predictions of burst pressures of these flaws, four different flaw shape idealization models, i.e., semi-elliptical, rectangular, maximum length with effective flaw depth and weakest subcrack model proposed by the Electric Power Research Institute (EPRI) are employed in this work. In order to validate the applicability of these idealization methods, the burst pressures of SG tubes with these flaws are investigated by using the finite element (FE) analyses. By comparing the predictions of the burst pressures based on the four different flaw shape idealization methods with those based on actual flaw shapes, it is found that the weakest subcrack model proposed by the EPRI and maximum length with effective flaw depth model provide the better agreement with actual complex flaw.

Common defects and contributing parameters in powder bed fusion AM process and their classification for online monitoring and control: a review

The powder bed fusion additive manufacturing process enables fabrication of metal parts with complex geometry and elaborate internal features, the simplification of the assembly process, and the reduction of development time; however, its tremendous potential for widespread application in industry is hampered by the lack of consistent quality. This limits its ability as a viable manufacturing process particularly in the aerospace and medical industries where high quality and repeatability are critical. A variety of defects, which may be initiated during powder bed fusion additive manufacturing, compromise the repeatability, precision, and resulting mechanical properties of the final part. One approach that has been more recently proposed to try to control the process by detecting, avoiding, and/or eliminating defects is online monitoring. In order to support the design and implementation of effective monitoring and control strategies, this paper identifies, analyzes, and classifies the common defects and their contributing parameters reported in the literature, and defines the relationship between the two. Next, both defects and contributing parameters are categorized under an umbrella of manufacturing features for monitoring and control purposes. The quintuple set of manufacturing features presented here is meant to be employed for online monitoring and control in order to ultimately achieve a defect-free part. This categorization is established based on three criteria: (1) covering all the defects generated during the process, (2) including the essential contributing parameters for the majority of defects, and (3) the defects need to be detectable by existing monitoring approaches as well as controllable through standard process parameters. Finally, the monitoring of signatures instead of actual defects is presented as an alternative approach to controlling the process “indirectly.”

Reliable Chip & Bond Wire QA Sampling, Counting & Inspection by Implementing Multiple Magnification AOI

Reliable Chip & Bond Wire QA Sampling, Counting & Inspection by Implementing Multiple Magnification AOI

Identification of the actual state and entity availability forecasting in power engineering using neural-network technologies

A growing number of severe accidents in RF call for the need to develop a system that could prevent emergency situations. In a number of cases accident rate is stipulated by careless inspections and neglects in developing repair programs. Across the country rates of accidents are growing because of a so-called “human factor”. In this regard, there has become urgent the problem of identification of the actual state of technological facilities in power engineering using data on engineering processes running and applying artificial intelligence methods. The present work comprises four model states of manufacturing equipment of engineering companies: defect, failure, preliminary situation, accident. Defect evaluation is carried out using both data from SCADA and ASEPCR and qualitative information (verbal assessments of experts in subject matter, photo- and video materials of surveys processed using pattern recognition methods in order to satisfy the requirements). Early identification of defects makes possible to predict the failure of manufacturing equipment using mathematical techniques of artificial neural network. In its turn, this helps to calculate predicted characteristics of reliability of engineering facilities using methods of reliability theory. Calculation of the given parameters provides the real-time estimation of remaining service life of manufacturing equipment for the whole operation period. The neural networks model allows evaluating possibility of failure of a piece of equipment consistent with types of actual defects and their previous reasons. The article presents the grounds for a choice of training and testing samples for the developed neural network, evaluates the adequacy of the neural networks model, and shows how the model can be used to forecast equipment failure. There have been carried out simulating experiments using a computer and retrospective samples of actual values for power engineering companies. The efficiency of the developed model for different types of manufacturing equipment has been proved. There have been offered other research areas in terms of the presented subject matter.

Improving the ultrasonic imaging of hydrogen-induced cracking using focused ultrasound

Hydrogen-induced cracking (HIC) is one of the main factors in collapses and breakages of structural steel in gas and oil industries. As one of the nondestructive evaluation techniques, Ultrasonic testing (UT) can play an extremely important role in identifying and ascertaining the size of HICs. Conventional UT techniques show relatively good detection performance but poor accuracy in terms of evaluating HICs due to the major problems caused by ultrasonic beam divergence and the existence of linked cracks. Therefore, developing C-scan imaging techniques and identifying stepwise cracks with UT scanning experiments are necessary for added information accuracy on HICs. In this study, double-faced and single-frequency C-scan imaging with Synthetic aperture focusing technique (SAFT) were applied to describe the images of HICs in specimens with the location and size information close to those of the actual defects and identify clustered parallel cracks. Moreover, the Technique for the identification of flaw signal using deconvolution (TIFD) was applied to determine the presence of stepwise cracks in the ultrasonic testing experiment using a 45-degree shear wave.

Acknowledging the Achilles' Heel: On the Micro-traumatic Impact of a Person's Actual Flaws, Deficits, and Vulnerabilities

Each of us is likely to have at least one “Achilles' heel”—an element of selfhood that is inherently weak and deficient relative to other elements within ourselves, and to the capabilities we regularly see in others. Psychoanalytic theory has given short shrift to the painful psychic impact of these actual (as opposed to only subjectively experienced) flaws and deficits. Society often downplays, “airbrushes,” or averts its eyes from an individual's evident deficiencies; but their very existence—not to mention their impact—can evoke underlying shame and create extensive difficulties for the individual.This article explores the ways one's Achilles' heels may have a micro-traumatic effect (Crastnopol, 2015) on oneself and others. The author discusses various constructive attitudes and approaches to grappling with ingrained flaws and limitations in the course of psychoanalytic work.

Profile imaging of actual defects in steel plate based on electromagnetic ultrasonic SH guided wave scattering

Defects occur almost inevitably during the service life of steel plate structures. Industrial safety requirements for steel plate structures are increasingly stringent and the need for defect imaging is urgent. This work proposes a profile imaging method aimed at actual defects in steel plate based on electromagnetic ultrasonic shear horizontal (SH) guided wave scattering. Firstly, a scattering model is proposed based on the characteristics of wave scattering employing the rectangular coordinate system. Scattering points and scattering edges are defined and calculated after the positions of scattering points are proved to be unique on the condition that the transmitting direction is known. Secondly, detailed procedures of the actual defect profile imaging method are provided, exploiting direction-controllable transmitters and omnidirectional receivers. Thirdly, an experimental platform on a steel plate with an actual defect is set up. Electromagnetic acoustic transducers based on SH guided waves are applied to stimulate and receive SH waves. The proposed profile imaging method and procedures are conducted on the experimental platform and a profile image of the actual defect is obtained. The error between the calculated profile and actual profile is then defined and displayed on the plane of the rectangular coordinate system. Results show that the profile image almost clings to the actual profile of the defect and the average profile error is only 4.3%. The proposed guided wave scattering model and profile imaging method are verified to be able to describe the profile of an actual defect in steel plate with high precision.

Plastic analysis for through silicon via with actual etching defect of triangular-teeth and scallops

Through silicon via (TSV) with triangular-teeth and scalloped side wall (TSSW) not only lowers down the electrical performance but also alters the mechanism of thermal mechanical stability. By considering the realistic etching defects on TSV side wall, a more reasonable and reliable three-dimension (3D) TSV model with TSSW is built. The thermo-mechanical issues induced by the unsmooth side wall of the TSV are studied by finite element method (FEM) in this work. With the presence of TSSW, much more tips in interfaces of the TSV-Cu and SiO2 and the SiO2 and Si are brought and the shear stress and normal stress are distinct comparing with the smooth side wall. Therefore, the thermo-mechanical mechanism of the TSV is changed greatly. By investigating the normal stress, shear stress and strain energy density (SED) of the triangular-teeth local region, it has been found that severe normal stress variation (−200–70MPa) and multiple variation of shear stress τxy contributes the peeling, slip and crack issues. The effective plastic strain, displacement and von Mises stress, and shear stress and normal stress in the X, Y and Z direction are studied in detail. The effect of the TSSW on the thermal stress and keep-out zone (KOZ) size is discussed.

Phase boundary between cubic B1 and rhombohedral structures in (Mg,Fe)O magnesiowüstite determined by in situ X-ray diffraction measurements

The phase relations and equation of state of (Mg0.08Fe0.92)O magnesiowustite (Mw92) have been studied using the Kawai-type high-pressure apparatus coupled with synchrotron radiation. To determine the phase boundary between the NaCl-type cubic (B1) and rhombohedral (rB1) structures in Mw92, in situ X-ray observations were carried out at pressures of 0–35 GPa and temperatures of 300–1473 K. Au and MgO were used as the internal pressure markers and metallic Fe as oxygen fugacity buffer. The phase boundary between B1 and rB1 structures was described by a linear equation P (GPa) = 1.6 + 0.033 × T (K). The Clapeyron slope (dP/dT) determined in this study is close to that obtained at pressures above 70 GPa but steeper than that obtained for FeO. An addition of MgO to FeO structure expands the stability field of the rB1 phase to lower pressures and higher temperatures. Thus, the rB1 phase may be stabilized with respect to the B1 phase at a lower pressures. The pressure–volume–temperature equation of state of B1-Mw92 was determined up to 30 GPa and 1473 K. Fitting the hydrostatic compression data up to 30 GPa with the Birch–Murnaghan equation of state (EoS) yielded: unit cell volume (V 0,T0), 79.23 ± 4 A3; bulk modulus (K 0,T0), 183 ± 4 GPa; its pressure derivative (K′ T ), 4.1 ± 0.4; (∂K 0,T /∂T) = −0.029 ± 0.005 GPa K‒1; a = 3.70 ± 0.27 × 10−5 K−1 and b = 0.47 ± 0.49 × 10−8 K−2, where α0,T = a + bT is the volumetric thermal expansion coefficient. The obtained bulk modulus of Mw92 is very close to the value expected for stoichiometric iron-rich (Mg,Fe)O. This result confirms the idea that the bulk modulus of (Mg,Fe)O is greatly affected by the actual defect structure, caused by either Mg2+ or vacancies.

Evaluating the exposure dose of 320-row area detector computed tomography and its reliability in the measurement of bone defect in alveolar cleft

ObjectiveThe usefulness of computed tomography (CT) for measuring bone defect volume in the alveolar cleft has attracted considerable attention in recent years. Moreover, 320-row area detector CT (320-ADCT) is currently widely utilized. This technology helps to acquire smooth three-dimensional images, with a reduced exposure dose and improved image resolution, similar to those of dental cone-beam CT (CBCT). We compared the exposure dose of 320-ADCT with that of dental CBCT, and analyzed the reliability of a volumetric method for measuring bone defects in the alveolar cleft using 320-ADCT, both experimentally and clinically. MethodsWe performed thermoluminescent dosimeter measurements using an anthropomorphic RANDO phantom. Additionally, we evaluated the reproducibility of the volumetric measurement method for the bone defective volume using CT images of simulated bone defects in pigs. In addition, we evaluated the inter-rater reliability of the volumetrically measured values of bone defects in 20 patients with a unilateral cleft. ResultsThe exposure doses of 320-row area detector CT and CBCT were similar. The errors of volumetric values between calculated and actual bone defects created in pigs were up to 7.6%. Inter-rater reliability of the calculated volumes of bone defect was evaluated as high, according to Bland–Altman analysis. ConclusionsCT examination with a low exposure dose and precise analysis is feasible without the use of highly specialized dental CBCT.

Loss of dynein-2 intermediate chain Wdr34 results in defects in retrograde ciliary protein trafficking and Hedgehog signaling in the mouse.

The Wdr34 gene encodes an intermediate chain of cytoplasmic dynein 2, the motor for retrograde intraflagellar transport (IFT) in primary cilia. Although mutations in human WDR34 have recently been reported, the association of WDR34 function with Hedgehog (Hh) signaling has not been established, and actual cilia defects in the WDR34 mutant cells have also not been completely characterized. In the present study, we show that Wdr34 mutant mice die in midgestation and exhibit open brain and polydactyly phenotypes. Several Hh-dependent ventral neural cell types are not specified in the mutant neural tube. The expression of the direct Hh targets, Gli1 and Patched 1, is inhibited, while the expression of limb patterning genes that are normally inhibited by the Gli3 repressor is anteriorly expanded in mutant limbs. Comparison of cilia phenotype and function among wild type, Dnchc2 (dynein 2 heavy chain), and Wdr34 mutant cells demonstrates that cilia in both Dnchc2 and Wdr34 mutant cells are stumpy. Several ciliary proteins examined abnormally accumulate in the cilia of both mutant cells. Consistent with its function, overexpressed Wdr34 is occasionally localized to cilia, and Wdr34 is required for the ciliary localization of dynein 2 light intermediate chain Lic3. More interestingly, we show that both Dnchc2 and Wdr34 act between Smo and Gli2/Gli3 in the Hh pathway. Therefore, like Dnchc2, Wdr34 is required for ciliogenesis, retrograde ciliary protein trafficking, and the regulation of Gli2/Gli3 activators and repressors. Furthermore, both Wdr34 and Dnchc2 promote microtubule growth, a novel dynein 2 function in a non-cilia structure.

High noise tolerance feature extraction for partial discharge classification in XLPE cable joints

Cable joints are the weakest point in cross-linked polyethylene (XLPE) cables and are susceptible to insulation failures. Partial discharge (PD) analysis is a vital tool for assessing the insulation quality in cable joints. Although many works have been done on PD pattern classification, it is usually performed in a noise-free environment. Also, works on PD pattern classification are mostly done on lab fabricated insulators, where works on actual cable joint defects are less likely to be found in literature. Therefore, in this work, classifications of cable joint defect types from partial discharge measurement under noisy environment were performed. Five XLPE cable joints with artificially created defects were prepared based on the defects commonly encountered on-site. A high noise tolerance principal component analysis (PCA)-based feature extraction was proposed and compared against conventional input features such as statistical and fractal features. These input features were used to train the classifiers to classify each defect type in the cable joint samples. Classifications were performed using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). It was found that the proposed PCA features displayed the highest noise tolerance with the least performance degradation compared to other input features under noisy environment.

Diagnosis of Performance Limiting Segments in Integrated Circuits Using Path Delay Measurements

An approach capable of identifying the locations of distributed small delay defects, arising due to manufacturing aberrations, is proposed. It is shown that the proposed formulation can be transformed into a Boolean satisfiability form to be solved by any satisfiability solver. The approach is capable of providing a small number of alternative sets of potential defective segments, and one of the solutions is the actual defect configuration. This is shown to be a very important property toward the effective identification of the defective segments. Experimental analysis on International symposium on circuits and systems and International Test Conference benchmark suites show that the proposed approach is highly scalable and identifies the location of multiple delay defects.

Non-uniformity correction and sound zone detection in pulse thermographic nondestructive evaluation

An important requirement in the thermographic nondestructive evaluation is the identification of actual sound zone or the base line with which the defective areas are compared to determine the actual temperature contrast and the corresponding defect severity. In a part under inspection, the actual sound zone is not known a priori and various approximations have been used in the past to serve as this unknown base line. Determination of actual sound zone in a defective test object is still a challenge. A related issue before the identification of this base line pixel is in the elimination of non-uniformity in the temperature distribution across the specimen surface which is unrelated to the actual defects. This spurious contrast is often introduced by the limitations of the instrumentation or the test procedure and it has to be eliminated before pixels in the sound zone can be located. This paper presents an automated procedure for simultaneously eliminating spurious contrast and locating sound zone pixels, directly from experimental data in a thermographic nondestructive evaluation. Location of actual sound zone pixels facilitates accurate thermal contrast computations, extraction of thermal properties such as break time and thermal diffusivity. In addition, based on the actual sound zone temperature profile it is possible to normalize experimental thermographic results in such a way that they can be directly correlated with results from numerical simulations.

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.