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Titanium alloys are extensively used in the manufacturing of key components in aerospace engines and aircraft structures due to their excellent properties. However, aircraft skins in harsh operating environments are subjected to long-term corrosion and pressure concentrations, which can lead to the formation of cracks and other defects. In this paper, a detection probe is designed based on the principle of alternating current field measurement, which can effectively detect both surface and buried defects in thin-walled titanium alloy plates. A finite element simulation model of alternating current field measurement detection for buried defects in thin-walled TC4 titanium alloy plates is established using COMSOL 5.6 software. The influence of defect length, depth, and excitation frequency on the characteristic signals is investigated, and the detection probe is optimized. Simulation and experimental results demonstrate that the proposed detection probe exhibits high detection sensitivity to varying lengths and depths of buried defects, and can detect small cracks with a length of 3 mm and a burial depth of 2 mm, as well as deep defects with a length of 10 mm and a burial depth of 4 mm. The feasibility of this probe for detecting buried defects in titanium alloy aircraft skin is confirmed. [ABSTRACT FROM AUTHOR]

Glass fiber-reinforced polymer (GFRP) is widely used in engineering fields involving aerospace, energy, transportation, etc. If internal buried defects occur due to hostile environments during fabrication and practical service, the structural integrity and safety of GFRP structures would be severely undermined. Therefore, it is indispensable to carry out effective quantitative nondestructive testing (NDT) of internal defects buried within GFRP structures. Along with the development of composite materials, microwave NDT is promising in non-intrusive inspection of defects in GFRPs. In this paper, quantitative screening of the subsurface impact damage and air void in a unidirectional GFRP via microwave reflectometry was intensively investigated. The influence of the microwave polarization direction with respect to the GFRP fiber direction on the reflection coefficient was investigated by using the equivalent relative permittivity calculated with theoretical analysis. Following this, a microwave NDT system was built up for further investigation regarding the imaging and quantitative evaluation of buried defects in GFRPs. A direct-wave suppression method based on singular-value decomposition was proposed to obtain high-quality defect images. The defect in-plane area was subsequently assessed via a proposed defect-edge identification method. The simulation and experimental results revealed that (1) the testing sensitivity to buried defects was the highest when the electric-field polarization direction is parallel to the GFRP fiber direction; and (2) the averaged evaluation accuracy regarding the in-plane area of the buried defect reached approximately 90% by applying the microwave reflectometry together with the proposed processing methods. [ABSTRACT FROM AUTHOR]

Magnetic permeability perturbation testing (MPPT) owns the advantages of high sensitivity and stability for buried defect, but the signals are always affected by the externally applied magnetic field. The mechanism of MPPT method for buried defect under unsaturated magnetization is mainly discussed in detail and verified by simulations. A series of experimental results show that the multi-source effect exists in the MPPT detection. The MPPT signal for buried defects under unsaturated magnetization depends on both of the local magnetic permeability perturbation and magnetic leakage field of the defects. The detection results of different plate thickness show that the external magnetization field significantly affected the MPPT signal, presenting a significant "convex-concave" nonlinear feature, which is helpful for the selection of magnetization current in practical detection. Clarifying the dominant mechanism of the MPPT signal for buried defect is of great significance to precision evaluation of cracks and related applications. [ABSTRACT FROM AUTHOR]

Deep trenches (DT) in silicon are a source of mechanical stress that can eventually lead to the formation of crystal defects. Herein this work, it is shown that room‐temperature micro‐photoluminescence imaging can be applied for a nondestructive in‐line monitoring of buried defects generated by DT. With the training of a convolutional neural network for automatic image segmentation, image analysis is automatized, allowing the inspection of large areas. The analysis of DT structures with various spacing and multiplicity reveals that the DT mutual distance is the main factor governing defects generation. Finally, a step‐by‐step analysis allows to detect the step responsible for defect generation and a further defect density increase along the process flow. [ABSTRACT FROM AUTHOR]

In this paper, the innovative photoluminescence imaging technique is described for applications to buried defect detection in silicon devices. The validity of this emerging technique is first assessed in comparison with well-established characterization techniques (defect selective etching of silicon, X-Ray diffraction topography, cross-sectional transmission electron microscopy imaging and photoluminescence spectroscopy). This paper then describes specific applications illustrating the use of the photoluminescence imaging technique for common processes of the CMOS semiconductor industry. The benefit of this fast, high resolution and non-destructive technique is demonstrated: this includes industrial use of the technique for in-line production control on product wafers. [ABSTRACT FROM AUTHOR]

Defect avoidance methods are likely to play a key role in overcoming the challenge of mask blank detectivity in extreme ultraviolet (EUV) lithography. In this work, we propose an innovative EUV mask defect avoidance method. It is the first approach that allows exploring all the degrees of freedom available for defect avoidance (pattern shift, rotation and mask floorplanning). We model the defect avoidance problem as a global, nonconvex optimization problem and then solve it using a combination of random walk and gradient descent. For a 8-nm polysilicon layer of an ARM Cortex M0 layout, our method achieves a 60% point better mask yield compared to prior art in defect avoidance for a 40-defect mask. We show that pattern shift is the most significant degree of freedom for improving mask yield. Rotation and mask floorplanning can also help improve mask yield to a Certain extent. [ABSTRACT FROM AUTHOR]

Defect avoidance methods are likely to play a key role in overcoming the challenge of mask blank defectivity in extreme ultraviolet (EUV) lithography. In this work, we propose an innovative EUV mask defect avoidance method. It is the first approach that allows exploring all the degrees of freedom available for defect avoidance (pattern shift, rotation and mask floorplanning). We model the defect avoidance problem as a global, nonconvex optimization problem and then solve it using a combination of random walk and gradient descent. For a 8-nm polysilicon layer of an ARM Cortex M0 layout, our method achieves a 60% point better mask yield compared to prior art in defect avoidance for a 40-defect mask. We show that pattern shift is the most significant degree of freedom for improving mask yield. Rotation and mask floorplanning can also help improve mask yield to a certain extent. [ABSTRACT FROM AUTHOR]

Fabricating defect-free mask blanks remains a major obstacle for the adoption of EUV lithography. We propose a simulated annealing based gridded floorplanner for single-project multiple-die reticles that minimize the design impact of buried defects. Our results show a substantial improvement in mask yield with this approach. For a 60-defect mask, our approach can improve the mask yield from 0% to 26%. If additional design information is available, it can be exploited for more accurate yield computation and further improvement in mask yield to 99.6%. These improvements are achieved with a limited area overhead of less than 0.2% on the exposure field. Our simulation results also indicate that around 10%–30% mask yield improvement is possible as a result of floorplanning compared to shifting the entire mask pattern. Our floorplanner can tolerate a defect position error (due to mask blank inspection tools) of 0.25 \mum with just a 2% reduction in yield. The impact of defect dimensions and multilayer EUV patterning on the viability of floorplanning is also analyzed in this paper. [ABSTRACT FROM PUBLISHER]

Abstract: A high resolution approach is proposed to quantitatively estimate the depth of defects buried in planar metallic structures. This approach associates a multiple signal characterization (MUSIC) algorithm with an original eddy current imager. The interactions of the eddy currents and the defects are modelled by a set of virtual magnetic sources propagating in a spherical manner up to the surface of the structure. The defect localization is carried out using the MUSIC algorithm, applied to the multi-frequency observation of the magnetic field distribution at the surface of the structure. Accurate results obtained on simulated and experimental data relative to defects buried down to 5.4mm in an aluminium layered structure validate the approach. [Copyright &y& Elsevier]

Abstract: The authors present an original eddy current imager (ECI) designed for the fast and accurate non-destructive evaluation of defects buried next to rivets in aeronautical lap-joints. The ECI is associated to a signal processing method based on a principal component analysis (PCA) followed by a maximum likelihood (ML) approach. The PCA was implemented using EC images obtained with selected excitation frequencies. These images are considered as resulting from a linear mixing of different sources including the presence of rivets and defects, and the PCA is used to separate these sources thanks to an eigen decomposition of the EC data covariance matrix. As a result, the defect signatures are enhanced and used to implement an automatic defect characterization. This characterization is carried out by the means of an ML approach which allows the length and depth of the defects to be estimated. The method was implemented for the evaluation of a laboratory made riveted lap joint mock-up featuring buried defects. It was experimentally optimized and successfully implemented for the characterization of calibrated defects ranging from 2 to 10mm in length and 2 to 8mm in depth. [Copyright &y& Elsevier]