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At least 37 records · Page 2

AE (Acoustic Emission) for Flip-Chip CGA/FCBGA Defect Detection

C-mode scanning acoustic microscopy (C-SAM) is a nondestructive inspection technique that uses ultrasound to show the internal feature of a specimen. A very high or ultra-high-frequency ultrasound passes through a specimen to produce a visible acoustic microimage (AMI) of its inner features. As ultrasound travels into a specimen, the wave is absorbed, scattered or reflected. The response is highly sensitive to the elastic properties of the materials and is especially sensitive to air gaps. This specific characteristic makes AMI the preferred method for finding "air gaps" such as delamination, cracks, voids, and porosity. C-SAM analysis, which is a type of AMI, was widely used in the past for evaluation of plastic microelectronic circuits, especially for detecting delamination of direct die bonding. With the introduction of the flip-chip die attachment in a package; its use has been expanded to nondestructive characterization of the flip-chip solder bumps and underfill. Figure 1.1 compares visual and C-SAM inspection approaches for defect detection, especially for solder joint interconnections and hidden defects. C-SAM is specifically useful for package features like internal cracks and delamination. C-SAM not only allows for the visualization of the interior features, it has the ability to produce images on layer-by-layer basis. Visual inspection; however, is only superior to C-SAM for the exposed features including solder dewetting, microcracks, and contamination. Ideally, a combination of various inspection techniques - visual, optical and SEM microscopy, C-SAM, and X-ray - need to be performed in order to assure quality at part, package, and system levels. This reports presents evaluations performed on various advanced packages/assemblies, especially the flip-chip die version of ball grid array/column grid array (BGA/CGA) using C-SAM equipment. Both external and internal equipment was used for evaluation. The outside facility provided images of the key features that could be detected using the most advanced C-SAM equipment with a skilled operator. Investigation continued using in-house equipment with its limitations. For comparison, representative X-rays of the assemblies were also gathered to show key defect detection features of these non-destructive techniques. Key images gathered and compared are: Compared the images of 2D X-ray and C-SAM for a plastic LGA assembly showing features that could be detected by either NDE technique. For this specific case, X-ray was a clear winner. Evaluated flip-chip CGA and FCBGA assemblies with and without heat sink by C-SAM. Only the FCCGA package that had no heat sink could be fully analyzed for underfill and bump quality. Cross-sectional microscopy did not revealed peripheral delamination features detected by C-SAM. Analyzed a number of fine pitch PBGA assemblies by C-SAM. Even though the internal features of the package assemblies could be detected, C-SAM was unable to detect solder joint failure at either the package or board level. Twenty times touch ups by solder iron with 700degF tip temperature, each with about 5 second duration, did not induce defects to be detected by C-SAM images. Other techniques need to be considered to induce known defects for characterization. Given NASA's emphasis on the use of microelectronic packages and assemblies and quality assurance on workmanship defect detection, understanding key features of various inspection systems that detect defects in the early stages of package and assembly is critical to developing approaches that will minimize future failures. Additional specific, tailored non-destructive inspection approaches could enable low-risk insertion of these advanced electronic packages having hidden and fine features.

Ghaffarian, Reza↗

Crystalline Si Surface Passivation with Nafion for Bulk Defects Detection with Electron Paramagnetic Resonance

Here, in monocrystalline Si (c-Si) solar cells, identification and mitigation of bulk defects are crucial to achieving a high photoconversion efficiency. To spectroscopically detect defects in the c-Si bulk, it is desirable to passivate the surface defects. Passivation of the c-Si surface with dielectrics such as Al 2 O 3 and SiNx requires deposition at elevated temperatures, which can influence defects in the bulk. Herein, we report on the passivation of different Czochralski (Cz) Si wafer surfaces by an organic copolymer, Nafion. We test the efficacy of the surface passivation at temperatures ranging from 6 to 473 K to detect bulk defects using electron paramagnetic resonance (EPR) spectroscopy. By comparing with state-of-the-art passivation layers, including Al 2 O 3 and liquid HF/HCl, we found that at room temperature, Nafion can provide comparable passivation of n-type Cz Si with an implied open-circuit voltage (iV oc ) of 713 mV and a recombination current prefactor J o of 5 fA/cm 2 . For p-type Cz Si, we obtained an iV oc of 682 mV with a J o of 22.4 fA/cm 2 . Scanning electron microscopy and photoluminescence reveal that Nafion can also be used to passivate the surface of c-Si solar cell fragments scribed from a solar cell module by using a laser. Consistent with previous studies, analysis of the EPR spectroscopy data confirms that the H-terminated surface is necessary, and fixed negative charge in Nafion is responsible for the field-effect passivation. While the surface passivation quality was maintained for almost 24 h, which is sufficient for spectroscopic measurements, the passivation degraded over longer durations, which can be attributed to surface SiO x growth. These results show that Nafion is a promising room-temperature surface passivation technique to study bulk defects in c-Si.

14 SOLAR ENERGY↗

The application of an optical Fourier spectrum analyzer on detecting defects in mass-produced satellite photographs

Various defects in mass-produced pictures transmitted to earth from a satellite are investigated. It is found that the following defects are readily detectable via Fourier spectrum analysis: (1) bit slip, (2) breakup causing loss of image, and (3) disabled track at the top of the imagery. The scratches made on the film during mass production, which are difficult to detect by visual observation, also show themselves readily in Fourier spectrum analysis. A relation is established between the number of scratches, their width and depth and the intensity of their Fourier spectra. Other defects that are found to be equally suitable for Fourier spectrum analysis or visual (image analysis) detection are synchronous loss without blurring of image, and density variation in gray scale. However, the Fourier spectrum analysis is found to be unsuitable for detection of such defects as pin holes, annotation error, synchronous loss with blurring of images, and missing image in the beginning of the work order. The design of an automated, real time system, which will reject defective films, is treated.

Athale, R.↗

Infrared thermography NDT for in-situ defect detection in sandwich composite panel manufacturing

Composite manufacturing presents numerous challenges, as defects can arise from various sources throughout the process. In sandwich composite structures, the integration of a foam core introduces additional complexity and increases the likelihood of defect formation like delamination. To mitigate these issues and reduce the risk of future structural failures, in-situ monitoring during manufacturing is essential. This study investigates infrared (IR) thermography as a non-destructive technique for detecting manufacturing defects in foam-core sandwich composite panels under thermally excited conditions representative of in-situ processing. A stationary FLIR A8590 IR camera (640 × 512 pixels, 30Hz, 17mm lens, 9 ft stand-off distance) was used to monitor prefabricated panels subjected to controlled external heating simulating compression molding and resin cure exotherm. Interlaminar delamination defects with characteristic sizes ranging from 0.25 × 0.25in² to 5 × 5in² produced measurable surface temperature depressions of approximately 4–10°C during transient cooling, exceeding the effective noise floor of the camera by more than two standard deviations. Thicker laminates exhibited prolonged defect detectability windows due to increased thermal diffusion time. In contrast, embedded Teflon inclusions generated weak thermal contrasts of ≤ 3°C, approaching the measurement noise floor, due to limited thermal property contrast with the surrounding glass fiber composite. These results establish quantitative detectability limits for stationary thermographic inspection of sandwich composite panels under manufacturing-representative thermal cycles.

Barakat, Abdallah [ORNL] (ORCID:0000000296141398)↗

How simple is software defect detection?

This study benchmarks several FSS techniques and reports several studies where a large set metrics were reduced to a handful with little loss of detection accuracy. This result raises the possibility that software defect detection may be much simpler than previously believed.

feature subset selection software fault model soft↗

Convolutional Autoencoder for Defect Detection in Additive Manufacturing

The core idea behind using machine learning (ML) for defect detection is that it can be used to detect flaws as they are being formed in an AM part. As the part is being made, a near-infrared (NIR) sensor records each layer and creates an image of the entire build layer. These images, usually thousands, can be compiled into a ‘3D’ array of the entire part. ML tools, such as a convolutional autoencoder (CAE) can go through these images and highlight potential anomalous regions of your part.

In-Situ Monitoring↗

Risk reduction using DDP (Defect Detection and Prevention): Software support and software applications

Risk assessment and mitigation is the focus of the Defect Detection and Prevention (DDP) process, which has been applied to spacecraft technology assessments and planning, both hardware and software. DDP's major elements and their relevance to core requirement engineering concerns are summarized. The accompanying research demonstration illustrates DDP's tool support, and further customizations for application to software.

Software-specific customizations↗

Localized Defect Detection from Spatially Mapped, In-Situ Process Data With Machine Learning

In powder bed fusion additive manufacturing, machines are often equipped with in-situ sensors to monitor the build environment as well as machine actuators and subsystems. The data from these sensors offer rich information about the consistency of the fabrication process within a build and across builds. This information may be used for process monitoring and defect detection; however, little has been done to leverage this data from the machines for more than just coarse-grained process monitoring. In this work we demonstrate how these inherently temporal data may be mapped spatially by leveraging scan path information. We then train a XGBoost machine learning model to predict localized defects—specifically soot–using only the mapped process data of builds from a laser powder bed fusion process as input features. The XGBoost model offers a feature importance metric that will help to elucidate possible relationships between the process data and observed defects. Finally, we analyze the model performance spatially and rationalize areas of greater and lesser performance.

3D printing↗

Automated Guided-Wave Scanning Developed to Characterize Materials and Detect Defects

The Nondestructive Evaluation (NDE) Group of the Optical Instrumentation Technology Branch at the NASA Glenn Research Center has developed a scanning system that uses guided waves to characterize materials and detect defects. The technique uses two ultrasonic transducers to interrogate the condition of a material. The sending transducer introduces an ultrasonic pulse at a point on the surface of the specimen, and the receiving transducer detects the signal after it has passed through the material. The aim of the method is to correlate certain parameters in both the time and frequency domains of the detected waveform to characteristics of the material between the two transducers. The scanning system is shown. The waveform parameters of interest include the attenuation due to internal damping, waveform shape parameters, and frequency shifts due to material changes. For the most part, guided waves are used to gauge the damage state and defect growth of materials subjected to various mechanical or environmental loads. The technique has been applied to polymer matrix composites, ceramic matrix composites, and metal matrix composites as well as metallic alloys. Historically, guided wave analysis has been a point-by-point, manual technique with waveforms collected at discrete locations and postprocessed. Data collection and analysis of this type limits the amount of detail that can be obtained. Also, the manual movement of the sensors is prone to user error and is time consuming. The development of an automated guided-wave scanning system has allowed the method to be applied to a wide variety of materials in a consistent, repeatable manner. Experimental studies have been conducted to determine the repeatability of the system as well as compare the results obtained using more traditional NDE methods. The following screen capture shows guided-wave scan results for a ceramic matrix composite plate, including images for each of nine calculated parameters. The system can display up to 18 different wave parameters. Multiple scans of the test specimen demonstrated excellent repeatability in the measurement of all the guided-wave parameters, far exceeding the traditional point-by-point technique. In addition, the scan was able to detect a subsurface defect that was confirmed using flash thermography This technology is being further refined to provide a more robust and efficient software environment. Future hardware upgrades will allow for multiple receiving transducers and the ability to scan more complex surfaces. This work supports composite materials development and testing under the Ultra-Efficient Engine Technology (UEET) Project, but it also will be applied to other material systems under development for a wide range of applications.

Martin, Richard E.↗

Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Abstract Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.

36 MATERIALS SCIENCE↗

Multi defect detection and analysis of electron microscopy images with deep learning

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. Furthermore, this study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.

36 MATERIALS SCIENCE↗

Frequency domain assurance criterion for defect detection in additively manufactured parts

Additive manufacturing (AM) is a very desirable manufacturing method for industries such as aerospace and automotive due to its ability to create internal features and complex geometries, however, these characteristics also present challenges for nondestructive evaluation (NDE) and defect detection. Dynamic analysis and modal analysis have shown promise for NDE and identifying flaws in AM parts. Frequency Domain Assurance Criterion (FDAC) is a method of dynamic evaluation that represents the degree of correlation between two sets of operational deflection shapes (ODS) at each spectral line. Using colormaps to display the results allows for quick and easy interpretation of parts’ similarities and differences. This method appears to show promise in identifying parts with minor defects and can be used to differentiate nominal and defective parts. This paper explores the use of FDAC on various AM parts with and without intentional defects.

Deonarain, G↗

Evolutionary Design of a Robotic Material Defect Detection System

During the post-flight inspection of SSME engines, several inaccessible regions must be disassembled to inspect for defects such as cracks, scratches, gouges, etc. An improvement to the inspection process would be the design and development of very small robots capable of penetrating these inaccessible regions and detecting the defects. The goal of this research was to utilize an evolutionary design approach for the robotic detection of these types of defects. A simulation and visualization tool was developed prior to receiving the hardware as a development test bed. A small, commercial off-the-shelf (COTS) robot was selected from several candidates as the proof of concept robot. The basic approach to detect the defects was to utilize Cadmium Sulfide (CdS) sensors to detect changes in contrast of an illuminated surface. A neural network, optimally designed utilizing a genetic algorithm, was employed to detect the presence of the defects (cracks). By utilization of the COTS robot and US sensors, the research successfully demonstrated that an evolutionarily designed neural network can detect the presence of surface defects.

Ballard, Gary↗

Lamb wave defect detection and evaluation using a fully non-contact laser system

Traditional Lamb wave structural health monitoring (SHM)/nondestructive evaluation (NDE) system employs contact type transducers such as PZT, ultrasonic transducers, and optical fibers. In application, transducer attachment and maintenance can be time and labor consuming. In addition, the use of couplant and adhesives can introduce additional materials on structures, and the interface coupling is often not well understood. To overcome these limitations, we proposed a fully non-contact NDE system by employing pulsed laser (PL) for Lamb wave actuation and scanning laser Doppler vibrometer (SLDV) for Lamb wave sensing. The proposed system is implemented on aluminum plates. The PL Lamb wave excitation is calibrated, and the optimal parameters are obtained. Lamb wave modes are then characterized through 1D wavefield analysis. With the calibrated and characterized system, defect detection and evaluation are achieved on aluminum plates with simulated defects (surfaced-bonded quartz rod, and machine milled crack) through 1D and 2D inspection in both time-space and frequency-wavenumber domains.

Ma, Zhaoyun↗

DEFECT DETECTION USING DYNAMIC ANALYSIS FOR ADDITIVE MANUFACTURED METALS

Additive manufacturing (AM) has the ability to produce parts with complex geometries and internal features, however, for demanding applications such as the automotive and aerospace industries, it is crucial that the parts can meet the demanding functional and geometric requirements. Quality control for AM parts focuses on nondestructive methods of testing, but many of the current methods are expensive and time-consuming. The research presented in this report explores various methods of nondestructive evaluation (NDE) using dynamic analysis on stainless steel parts produced with selective laser melting (SLM). Methods include, but are not limited to, frequency response functions (FRF), impedance-based measurements, and scanning laser doppler vibrometry. Additionally, mode shape analysis was performed in MATLAB and FEA simulations were used for comparison with experimental results. The results indicate that dynamic analysis has the potential to be a feasible method of defect detection and NDE in AM parts and future work should focus on refining these methods, such as optimizing test parameters to improve sensitivity to defects.

Deonarain, Gita↗

Product Defect Detection System: SYSM- 5620 Final Project

Retail sales is a growing market estimated to up to seven percent year over year. With this growing market there is also a trend in growing rate of retail returns, estimated just last year at $\$$850 billion. Retail stores must ensure that products available for purchase remain safe, undamaged, and acceptable to customers throughout their time in the store. This job exists regardless of the specific solution used because stores are always responsible for preventing damaged or defective products from reaching customers and when they fail to this is categorized under operation inefficiencies which accounts for an estimated $\$$12 billion in returns. When defective items remain on the sales floor, stores may experience increased returns, reduced customer satisfaction, loss of customer trust, and potential safety concerns depending on the product type. As a result, the core job to be done is to identify defective products quickly, remove them from the sales floor before they are purchased, and preserve useful information about the defect so that the store can improve its handling, stocking, and supplier coordination over time. The need for a more reliable process is especially important in high volume retail environments where employees manage large numbers of products across many aisles, shelves, and storage areas. In these settings, manual inspection alone can be inconsistent and difficult to sustain at the individual item level. At the same time, broader retail trends continue to emphasize operational efficiency, product visibility, and improved customer experience, creating an opportunity for more automated and data driven defect detection methods.

42 ENGINEERING↗

Method and System for Multi-Path Active Defect Detection, Localization and Characterization with Ultrasonic Guided Waves

A method and system of detecting, localizing, and characterizing a defect at one or more spatial points of interest on a structure. The method may include collecting first data in a first state using one or more transducers on the structure, collecting second data in a second state subsequent to the first state, computing a scattered impulse response based on the collected first data and the collected second data, comparing the scattered impulse response with an estimated scattered impulse response corresponding to the case when damage is present at one or more spatial points of interest on the structure, and combining the generated comparison results to detect, localize, and characterize a defect at the one or more spatial points of interest on the structure.

Hall, James Stroman↗

Thermographic imaging for high-temperature composite materials: A defect detection study

The ability of a thermographic imaging technique for detecting flat-bottom hole defects of various diameters and depths was evaluated in four composite systems (two types of ceramic matrix composites, one metal matrix composite, and one polymer matrix composite) of interest as high-temperature structural materials. The holes ranged from 1 to 13 mm in diameter and 0.1 to 2.5 mm in depth in samples approximately 2-3 mm thick. The thermographic imaging system utilized a scanning mirror optical system and infrared (IR) focusing lens in conjunction with a mercury cadmium telluride infrared detector element to obtain high resolution infrared images. High intensity flash lamps located on the same side as the infrared camera were used to heat the samples. After heating, up to 30 images were sequentially acquired at 70-150 msec intervals. Limits of detectability based on depth and diameter of the flat-bottom holes were defined for each composite material. Ultrasonic and radiographic images of the samples were obtained and compared with the thermographic images.

Roth, Don J.↗