Defect Mapping Research Articles

D2-SPDM: Faster R-CNN-Based Defect Detection and Surface Pixel Defect Mapping with Label Enhancement in Steel Manufacturing Processes

The steel manufacturing process is inherently continuous, meaning that if defects are not effectively detected in the initial stages, they may propagate through subsequent stages, resulting in high costs for corrections in the final product. Therefore, detecting surface defects and obtaining segmentation information is critical in the steel manufacturing industry to ensure product quality and enhance production efficiency. Specifically, segmentation information is essential for accurately understanding the shape and extent of defects, providing the necessary details for subsequent processes to address these defects. However, the time-consuming and costly process of generating segmentation annotations poses a significant barrier to practical industrial applications. This paper proposes a cost-efficient segmentation labeling framework that combines deep learning-based anomaly detection and label enhancement to address these challenges in the steel manufacturing process. Using ResNet-50, defects are classified, and faster region convolutional neural networks (faster R-CNNs) are employed to identify defect types and generate bounding boxes indicating the defect locations. Subsequently, recursive learning is performed using the GrabCut algorithm and the DeepLabv3+ model based on the generated bounding boxes, significantly reducing annotation costs by generating segmentation labels. The proposed framework effectively detects defects and accurately defines them, even in randomly collected images from the steel manufacturing process, contributing to both quality control and cost reduction. This study presents a novel approach for improving the quality of the steel manufacturing process and is expected to enhance overall efficiency in the steel manufacturing industry.

Radar forward modeling and intelligent identification of shallow subgrade defects

Geological radar is the primary nondestructive testing method for evaluating shallow subgrade defects. However, the radar atlas contains a large amount of information, and the efficiency of manual data interpretation and processing is low. In this study, the characteristics of radar maps of different defects were analyzed via forward simulation using finite-difference time-domain technology. The instantaneous characteristic information of different defect maps was integrated using map post-processing technology to improve recognition and translation accuracy. Finally, the convolution neural network algorithm was used to conduct data recognition to achieve the intelligent recognition of subgrade defects with an average detection accuracy of 73.93 % based on the radar subgrade defect atlas dataset, and the results were practically verified. The results show that the developed approach can accurately distinguish subgrade shallow defect information in the radar atlas. This approach is useful for accurate and efficient identification of latent highway defects.

Practical Improvement of Noncontact Production Monitoring of Doping in SiC Wafers with Extended Epilayer Defects

We discuss two defect related practical improvements in the corona noncontact CV metrology, (CnCV) for SiC. The improvements are introduced in response to requests from industrial tool users. The first improvement quantifies mapping of electrically active defects with the QUAD technique (Quality, Uniformity, and Defects). It provides the capability of user selectable die grids directly comparable with Near UV-PL and optical defect mapping. This shall enhance understanding of the device killer defects and help to correlate epilayer defects and device yield. The second improvement introduces auto-remeasurement of outliers appearing in doping measurements on defective sites. This procedure is analogous to that used in the Hg probe technique and it provides a means for correcting defect related distortions in SPC doping monitoring charts.

Laser Beam Induced Charge Collection for Defect Mapping and Spin State Readout in Diamond

Laser Beam Induced Charge Collection for Defect Mapping and Spin State Readout in Diamond

3D surface defect map for assessing buccolingual profile of single tooth gaps following alveolar ridge preservation.

A new, non-invasive approach suggests using single intraoral optical scanning to analyze the ridge profile of single-tooth gaps following alveolar ridge preservation in the absence of a baseline scan. This method involves creating a three-dimensional (3D) surface map to identify and assess contour changes and ridge profiles based on the adjacent teeth. The present study was designed as a cross-sectional pilot analysis on a convenience sample of patients undergoing alveolar ridge preservation. Intraoral optical scans were taken on 23 patients, capturing data from 30 edentulous sites. The digital models were then imported into an image analysis software for a 3D surface defect map analysis performed by one examiner. This analysis characterized the buccolingual profile of the single tooth gap relative to the adjacent teeth. 10 linear divergence points, spaced 0.5 mm apart in a corona-apical direction, were identified at the midfacial aspect of the sites. Based on these points the sites were plotted and grouped in three different buccolingual profiles (linear, concave, and convex). Clinical parameters including Keratinized mucosa Width (KMW), and soft tissue phenotype with Colorvue biotype probes were also recorded. Three different buccolingual patterns (linear, convex, and concave) were identified. Seven sites exhibited a linear profile, 10 sites displayed a concave shape, and 13 showed a convex profile. The linear profile had surface discrepancies similar to the neighboring teeth. In contrast, the convex profile revealed mid-buccal discrepancy localized only at the crestal aspect, while the concave had an extended divergence ranging from 1 to 5 mm below the soft tissue margin. Univariate and multiple logistic regression analyses did not reveal any statistically significant variables influencing profilometric analysis; however, when combining phenotype and KMW, thick phenotypes demonstrated a higher proportion of concavity (OR = 4.83) compared to thin ones, suggesting a significant trend. With every 1 mm of increase in KMW, the probability of showing a concavity decreased (p = 0.057). A 3D surface defect map represents a useful tool for objectively quantifying ridge defects and profiles by assessing profilometric and surface differences compared to adjacent dentition using a single intraoral scan. This method also indicates that KMW may play a critical role in preventing concavity defects. The 3D defect map can guide decision-making during soft tissue augmentation procedures by emphasizing the specific location of the defect and providing more detailed insights into its localization. These parameters can enable the tailoring of flap management and soft tissue grafting strategies to address the patient's individual needs.

Free-breathing 3D phase-resolved functional lung MRI vsbreath-hold hyperpolarized 129Xe ventilation MRI in patients with chronic obstructive pulmonary diseaseand healthy volunteers.

3D phase-resolved functional lung (PREFUL) MRI offers evaluation of pulmonary ventilation without inhalation of contrast agent. This study seeks to compare ventilation maps obtained from 3D PREFUL MRI with a direct ventilation measurement derived from 129Xe MRI in both patients with chronic obstructive pulmonary disease (COPD) and healthy volunteers. Thirty-one patients with COPD and 12 healthy controls underwent free-breathing 3D PREFUL MRI and breath-hold 129Xe MRI at 1.5 T. For both MRI techniques, ventilation defect (VD) maps were determined and respective ventilation defect percentage (VDP) values were computed. All parameters of both techniques were compared by Spearman correlation coefficient (r) and the differences between VDP values were quantified by Bland-Altman analysis and tested for significance using Wilcoxon signed-rank test. In a regional comparison of VD maps, spatial overlap and Sørensen-Dice coefficients of healthy and defect areas were computed. On a global level, all 3D PREFUL VDP values correlated significantly to VDP measure derived by 129Xe ventilation imaging (all r > 0.65; all p < 0.0001). 129Xe VDP was significantly greater than 3D PREFUL derived VDPRVent (mean bias = 10.5%, p < 0.001) and VDPFVL-CM (mean bias = 11.3%, p < 0.0001) but not for VDPCombined (mean bias = 1.7%, p = 0.70). The total regional agreement of 129Xe and 3D PREFUL VD maps ranged between 60% and 63%. Free-breathing3D PREFUL MRI showed a strong correlation with breath-hold hyperpolarized 129Xe MRI regarding the VDP values and modest differences in the detection of VDs on a regional level. 3D PREFUL MRI correlated with 129Xe MRI, unveiling regional differences in COPD defect identification. This proposes 3D PREFUL MRI as a ventilation mapping surrogate, eliminating the need for extra hardware or inhaled gases. Current non-invasive evaluation techniques for lung diseases have drawbacks; 129Xe MRI is limited by cost and availability. 3D PREFUL MRI correlated with 129Xe MRI, with regional differences in identifying COPD defects. 3DPREFUL MRI can provide ventilation mapping without the need for additional hardware or inhaled gases.

The effects of graphene intrinsic defects on the formation of extrinsic defects by plasma treatment

Two-dimensional materials, like graphene, are expected to make highly efficient permeation membranes for separation and sensing applications; however, defects of a specific size and location must be added to the film to make them selectively permeable. Though techniques like TEM drilling give precise information on size and location, this method is not scalable for eventual commercial use. Plasma treatments are a scalable alternative, but there is less information on where the defect formation begins and how intrinsic defects affect pore formation. This work seeks to understand these effects by using Raman spectroscopy at the same location before and after plasma treatments to map how the intrinsic defects affect the formation of extrinsic defects to create nanopores. Defect density increases with increasing RF power, independent of the gas used during the plasma treatment, suggesting that the mechanism is due to ion bombardment. Coalesced and arrested graphene syntheses are used to study the relative trends of defect formation and map existing graphene grain boundary locations. The results show that regions starting with high densities of intrinsic defects, potentially due to strain, were found to degrade into nanocrystalline graphene first on the amorphization trajectory, even before grain boundaries.

Flexible anisotropic magnetoresistive sensors for novel magnetic flux leakage testing capabilities

Rigid magnetic field sensors such as anisotropic magnetoresistive (AMR), giant magnetoresistive (GMR) and Hall sensors have been used for years and have become industry standard for electromagnetic non-destructive testing (NDT). Recent technological developments in the field of flexible electronics allow for the fabrication of reshapeable magnetic field sensors on flexible substrates via thin-film deposition or printing. The magnetic properties of these sensors have comparable characteristics to industry-standard rigid magnetic field sensors, with the added ability of adapting to the surface of complex components and scanning in contact with the sample surface. This improves defect detectability and magnetic signal strength by minimizing the scanning lift-off (LO) distance. In this article flexible AMR sensors mounted on a rotative mechanical holder were used to scan a semi-circular ferromagnetic sample with 3 reference defects via magnetic flux leakage (MFL) testing, thus demonstrating the applicability of this type of sensors for the scanning of curved samples. In order to benchmark the performance of these sensors in comparison to industry standard rigid magnetic field sensors, a ferromagnetic sample with 10 reference defects of different depths was scanned employing flexible AMR and rigid GMR sensors. Defects with depths ranging from 110μm up to 2240μm were detected with an signal-to-noise ratio (SNR) of 2.7 up to 27.9 (for flexible AMR sensors) and 6.2 up to 72.3 (for rigid GMR sensors), respectively. A 2D magnetometer mapping of the sample with a spatial scanning step of 10×50μm2 (flexible AMR) and 16×100μm2 (rigid GMR) was obtained. The results show that this type of sensor can be used for high-resolution and high-detail mapping of defects on the surface of planar and non-planar ferromagnetic samples since the scanning lift-off distance is equal to the substrate thickness of 20μm for in-contact scanning. The SNR comparison between flexible and rigid sensors shows that the performance of the flexible AMR sensors employed is not very far behind the performance of the rigid GMR sensors used.

P‐59: Deep Learning‐based Defect Map Classification and Similarity Search in Display Manufacturing

Defect map in glass substrates provides valuable information for process management, but statistical mapping classification using the number of defects by location within the glass has limited utility due to difficulties in classifying various types and low accuracy. A deep learning method is proposed to classify defect maps in glass substrates for display manufacturing. The method converts the defect maps into colored images and applies argumentation based on location‐based symmetry information. The method achieves 96.1% accuracy and 88.6% F1 score for minority defect types, and enables similarity search for quality management.

Research on intelligent classification of weld defects based on improved ResNeXt network

Welding technology is widely used in the connection of metal workpieces, and then due to improper operation and other reasons, small defects may occur in the welding, which will lead to welding failure and hazardous accidents. Therefore, it is very important to accurately identify welding defects. The automatic defect identification method based on deep learning learns features directly from the full-focus mapping of weld defects and automatically classifies them; the ResNeXt network is selected for improvement, and the SK convolution unit is introduced into the original ResNeXt-50 network model to enhance the adaptive adjustment of sensory field; in order to alleviate the problem of the point-by-point convolution in the ResNeXt-50 which consumes a large amount of In order to alleviate the problem that point-by-point convolution in ResNeXt-50 consumes a large amount of computation, 1×1 group convolution is used instead of the original point-by-point convolution, and channel rearrangement is introduced to alleviate the problem of weakened information exchange of groups caused by group convolution. After improvement, the ResNeXt-50 network model can achieve a classification accuracy of 98.2%, which is 5.5% more than that of the original ResNeXt-50 on this paper's dataset, and the classification accuracy after T-sne visualization also has obvious separation boundary and clustering effects.

A new ViT-Based augmentation framework for wafer map defect classification to enhance the resilience of semiconductor supply chains

Wafer map defect classification plays a crucial role in sustaining the semiconductor supply chain during industrial disruptions by ensuring continuity, resilience, and efficiency while aligning with sustainability principles. Up to 30 percent of production costs is lost to chip testing and yield losses for semiconductor manufacturing supply chain. In the semiconductor practice, wafer map defects recognition plays a linchpin role in the front-end-of-line stage. Defect pattern recognition can directly pinpoint the assignable causes and provide the domain experts with actionable insights. However, various wafer defect types occur differently from each other, which makes the collected wafer map dataset to be highly imbalanced in defect classes. In the face of data imbalance, the classification model usually cannot provide satisfactory classification performance. In this aspect, this paper intends to investigate the wafer defect map classification problem by using Vision Transformer (ViT) as an alternative data augmentation approach. The primary purpose is to alleviate the class-imbalance issue and then the performance of a deep learning-based convolutional neural network for wafer defect classification can be effectively improved. The experimental results demonstrate that the proposed data augmentation method by using ViT proves to be a potential generative model for improving wafer map defect classification, particularly robust on the individual minority class. In a word, the proposed augmentation framework for wafer map defect classification facilitates a more targeted and efficient approach to quality control, resource utilization, and production optimization in maintaining a sustainable semiconductor supply chain, particularly in times of industrial disruption.

Sample-imbalanced wafer map defects classification based on auxiliary classifier denoising diffusion probability model

This paper research on the classification task of wafer map defects under imbalanced sample. To improve the number of imbalanced wafer maps, a denoising diffusion probabilistic model with auxiliary classifier is proposed to generate new wafer maps. The developed model has an U-net structure with multiple inputs and outputs, taking wafer images, noise degree and category features as inputs, and outputs predicted noise and prediction category. When performing data augmentation, the trained model gradually removes prediction noise from the initial random noise and obtains new wafers. Finally, the balanced wafer defect maps are classified using a residual neural network. The proposed auxiliary classifier denoising diffusion probabilistic model and residual neural networks (ACDDPM-ResNet) method has validated on the MIR-WM811K dataset and MixedWM38 dataset, and the defect classification results of the imbalanced wafer map are remarkable improved after data augmentation. In addition, the paper also discusses and analyzes the influence of the maximum noise addition steps and the data augmentation size on the accuracy of wafer classification. It is further validated that the proposed wafer map classification method based on data augmentation can solve the classification problem caused by sample imbalance.

Multi-type classification and 3D visualization of internal defects in concrete plates based on a hybrid method of deep learning algorithm and bending mode analysis for acoustic vibration signals

In this paper, a hybrid method combining the deep learning algorithm and bending mode analysis of acoustic vibration signals is proposed for the multi-type classification and 3D visualization of internal defects in concrete plates. A novel deep learning model termed fully convolutional network based on principal component analysis and attention-embedded long-short term memory (PCA-ALSTM-FCN) is established. The PCA-ALSTM-FCN model successfully classifies multi-type internal defects, and the two-dimensional (2D) defect contour maps are generated based on the predicted state by the trained model. The defect depths are calculated according to the analytical formula of bending vibration mode of acoustic vibration signals. Combining the 2D defect map and depth information, the three-dimensional (3D) visualization images of defects are obtained. The average recognition accuracy of PCA-ALSTM-FCN model for different types of defects reaches 94.8 %, and the defect depth calculation error range is approximately 10 %–20 %. The experimental results show that the proposed method in this paper can accurately distinguish different types of internal defects and effectively locate the 3D position of defects in concrete.

Auto-Labeling for Pattern Recognition of Wafer Defect Maps in Semiconductor Manufacturing

Abstract In practical semiconductor processes, the defect analysis for wafer map is a critical step for improving product quality and yield. These defect patterns can provide important process information so that the process engineers can identify the key cause of process anomalies. However, in supervised learning, the manual annotation for wafer maps is an extremely exhausting task, and it can also induce misjudgment when a long-term operation is implemented. Toward this end, this paper proposes a new auto-labeling system based on ensemble classification. The noted VGG16 model is used in ensemble learning as the building block to train the classifier via a limited number of labeled data. Through the model being trained, the auto-labeling procedure is executed to annotate abundant unlabeled data. Therefore, the classification performances between the models trained by supervised and semi-supervised learning can be compared. In addition, the gradient-weighted class activation mapping (Grad-CAM) is also adopted to analyze and verify the quality of auto-labeling by visual inspection. Based on the experimental results, the proposed auto-label system can return a satisfactory classification performance, and then, the manual labeling operation can be drastically reduced. The classification performance for wafer defect patterns can be further assured as the auto-labeled data are given with corresponding confidence scores of specific defect patterns being identified in this study.

A Georeferenced Dataset for Mapping and Assessing Subgrade Defects in China’s High-Speed Railways

China has the world’s longest high-speed rail (HSR) network, marked by dense transportation and complex operations. However, frequent train use coupled with extreme weather conditions has led to rising subgrade issues. Existing railway defect records suffer from inconsistency, hindering direct applicability. Currently, there is a lack of a relevant dataset dedicated to HSR subgrade defects. To bridge this gap, we developed a comprehensive georeferenced dataset that encompasses defect records extracted from peer-reviewed literature published between 1999 and 2023 in China. Rigorous quality control procedures were implemented to eliminate duplicate data and ensure the accuracy of the dataset. The dataset consists of georeferenced records for eight different defects, spanning across 661 locations and categorized at various scales, ranging from provinces to townships. The most commonly reported defect types include subgrade settlement, frost damage, uplift deformation, and mud pumping. This dataset provides a comprehensive map of historical subgrade defects affecting high-speed railways in China. It could facilitate operational risk assessments and the prediction of subgrade performance.

Built environment defect mapping, modeling, and management (D3M): A BIM-based integrated framework

Built environment defect mapping, modeling, and management (D3M): A BIM-based integrated framework

Comparison of Free-Breathing 3D Phase-Resolved Functional Lung (PREFUL) MRI With Dynamic 19 F Ventilation MRI in Patients With Obstructive Lung Disease and Healthy Volunteers.

Non-contrast-enhanced 1 H magnetic resonance imaging (MRI) with full lung coverage shows promise for assessment of regional lung ventilation but a comparison with direct ventilation measurement using 19 F MRI is lacking. To compare ventilation parameters calculated using 3D phase-resolved functional lung (PREFUL) MRI with 19 F MRI. Prospective. Fifteen patients with asthma, 14 patients with chronic obstructive lung disease, and 13 healthy volunteers. A 3D gradient-echo pulse sequence with golden-angle increment and stack-of-stars encoding at 1.5 T. All participants underwent 3D PREFUL MRI and 19 F MRI. For 3D PREFUL, static regional ventilation (RVent) and dynamic flow-volume cross-correlation metric (FVL-CM) were calculated. For both parameters, ventilation defect percentage (VDP) values and ventilation defect (VD) maps (including a combination of both parameters [VDPCombined ]) were determined. For 19 F MRI, images from eight consecutive breaths under volume-controlled inhalation of perfluoropropane were acquired. Time-to-fill (TTF) and wash-in (WI) parameters were extracted. For all 19 F parameters, a VD map was generated and the corresponding VDP values were calculated. For all parameters, the relationship between the two techniques was assessed using a Spearman correlation (r). Differences between VDP values were compared using Bland-Altman analysis. For regional comparison of VD maps, spatial overlap and Sørensen-Dice coefficients were computed. 3D PREFUL VDP values were significantly correlated to VDP measures by 19 F (r range: 0.59-0.70). For VDPRVent , no significant bias was observed with VDP of the third and fourth breath (bias range = -6.8:7.7%, P range = 0.25:0.30). For VDPFVL-CM , no significant bias was found with VDP values of fourth-eighth breaths (bias range = -2.0:12.5%, P range = 0.12:0.75). The overall spatial overlap of all VD maps increased with each breath, ranging from 61% to 81%, stabilizing at the fourth breath. 3D PREFUL MRI parameters showed moderate to strong correlation with 19 F MRI. Depending on the 3D PREFUL VD map, the best regional agreement was found to 19 F VD maps of third-fifth breath. 3 TECHNICAL EFFICACY: Stage 2.

Echo time-dependent observed T1 and quantitative perfusion in chronic obstructive pulmonary disease using magnetic resonance imaging.

Due to hypoxic vasoconstriction, perfusion is interesting in the lungs. Magnetic Resonance Imaging (MRI) perfusion imaging based on Dynamic Contrast Enhancement (DCE) has been demonstrated in patients with Chronic Obstructive Pulmonary Diseases (COPD) using visual scores, and quantification methods were recently developed further. Inter-patient correlations of echo time-dependent observed T1 [T1(TE)] have been shown with perfusion scores, pulmonary function testing, and quantitative computed tomography. Here, we examined T1(TE) quantification and quantitative perfusion MRI together and investigated both inter-patient and local correlations between T1(TE) and quantitative perfusion. 22 patients (age 68.0 ± 6.2) with COPD were examined using morphological MRI, inversion recovery multi-echo 2D ultra-short TE (UTE) in 1-2 slices for T1(TE) mapping, and 4D Time-resolved angiography With Stochastic Trajectories (TWIST) for DCE. T1(TE) maps were calculated from 2D UTE at five TEs from 70 to 2,300 μs. Pulmonary Blood Flow (PBF) and perfusion defect (QDP) maps were produced from DCE measurements. Lungs were automatically segmented on UTE images and morphological MRI and these segmentations registered to DCE images. DCE images were separately registered to UTE in corresponding slices and divided into corresponding subdivisions. Spearman's correlation coefficients were calculated for inter-patient correlations using the entire segmented slices and for local correlations separately using registered images and subdivisions for each TE. Median T1(TE) in normal and defect areas according to QDP maps were compared. Inter-patient correlations were strongest on average at TE2 = 500 μs, reaching up to |ρ| = 0.64 for T1 with PBF and |ρ| = 0.76 with QDP. Generally, local correlations of T1 with PBF were weaker at TE2 than at TE1 or TE3 and with maximum values of |ρ| = 0.66 (from registration) and |ρ| = 0.69 (from subdivision). In 18 patients, T1 was shorter in defect areas than in normal areas, with the relative difference smallest at TE2. The inter-patient correlations of T1 with PBF and QDP found show similar strength and TE-dependence as those previously reported for visual perfusion scores and quantitative computed tomography. The local correlations and median T1 suggest that not only base T1 but also the TE-dependence of observed T1 in normal areas is closer to that found previously in healthy volunteers than in defect areas.

Industrial Product Surface Anomaly Detection with Realistic Synthetic Anomalies Based on Defect Map Prediction.

The occurrence of anomalies on the surface of industrial products can lead to issues such as decreased product quality, reduced production efficiency, and safety hazards. Early detection and resolution of these problems are crucial for ensuring the quality and efficiency of production. The key challenge in applying deep learning to surface defect detection of industrial products is the scarcity of defect samples, which will make supervised learning methods unsuitable for surface defect detection problems. Therefore, it is a reasonable solution to use anomaly detection methods to deal with surface defect detection. Among image-based anomaly detection, reconstruction-based methods are the most commonly used. However, reconstruction-based approaches lack the involvement of defect samples in the training process, posing the risk of a perfect reconstruction of defects by the reconstruction network. In this paper, we propose a reconstruction-based defect detection algorithm that addresses these challenges by utilizing more realistic synthetic anomalies for training. Our model focuses on creating authentic synthetic defects and introduces an auto-encoder image reconstruction network with deep feature consistency constraints, as well as a defect separation network with a large receptive field. We conducted experiments on the challenging MVTec anomaly detection dataset and our trained model achieved an AUROC score of 99.70% and an average precision (AP) score of 99.87%. Our method surpasses recently proposed defect detection algorithms, thereby enhancing the accuracy of surface defect detection in industrial products.

Time-averaged atomic volume spectrum: locating and identifying vacancies.

Vacancies, as well as their derivatives, usually play a crucial role in many essential properties of materials. However, they always behave erratically, especially under universal thermal vibration, and are therefore difficult to accurately locate. Until now, the lack of an accurate and flexible method for locating and identifying vacancies has hampered the development of relative fields. In this paper, we present a new method to solve this problem. The strategy is to target the atomic cage enwrapping vacancies instead of the vacancies themselves. The core of the method is a time-averaged atomic volume spectrum (TAVS). The key to this method is to identify atoms using time-averaged rather than transient atomic volume, thereby simultaneously denoising intrinsic thermal vibration and avoiding vacancy migration jump. Using this method, we have succeeded for the first time in obtaining the panoramic maps of spontaneously trapped defects in quenched and annealed face-centered cubic aluminum and even the instantaneous images of a steady trapping process. All characteristics of each trapped vacancy, including location, dimension, volume and morphology, as well as aggregate statistical data such as vacancy amount and concentration, can be completely and accurately obtained. Furthermore, these first maps of defects (vacancies) revealed some surprising and interesting phenomena for future exploration. In conclusion, this method provides not only a means of locating and catching vacancies, but also a strategy for identifying and characterizing vacancies. On the basis of its successful application in FCC Al, the TAVS method can be easily extended to other systems as well.