Failure Analysis Techniques Research Articles

Inversion for Thermal Properties with Frequency Domain Thermoreflectance.

3D integration of multiple microelectronic devices improves size, weight, and power while increasing the number of interconnections between components. One integration method involves the use of metal bump bonds to connect devices and components on a common interposer platform. Significant variations in the coefficient of thermal expansion in such systems lead to stresses that can cause thermomechanical and electrical failures. More advanced characterization and failure analysis techniques are necessary to assess the bond quality between components. Frequency domain thermoreflectance (FDTR) is a nondestructive, noncontact testing method used to determine thermal properties in a sample by fitting the phase lag between an applied heat flux and the surface temperature response. The typical use of FDTR data involves fitting for thermal properties in geometries with a high degree of symmetry. In this work, finite element method simulations are performed using high performance computing codes to facilitate the modeling of samples with arbitrary geometric complexity. A gradient-based optimization technique is also presented to determine unknown thermal properties in a discretized domain. Using experimental FDTR data from a GaN-diamond sample, thermal conductivity is then determined in an unknown layer to provide a spatial map of bond quality at various points in the sample.

Utilizing Fourier Transform Infrared Spectroscopy for Plastics Failure Analysis

Abstract This article explores one of the most commonly used analytical techniques in plastic component failure analysis—Fourier transform infrared spectroscopy—along with a case study to demonstrate its use.

Conventional Dissolved Gases Analysis in Power Transformers: Review

Transformers insulated with mineral oil tend to form gases, which might be caused by system faults or extended use. Based on an evaluation of the main failure analysis techniques using combustible gases, this study reviewed the conventional techniques for Dissolved Gas Analysis (DGA), present in the norms IEC 60599 and IEEE Std C57.104, and their failure analysis tendency. Furthermore, to illustrate distinct technique performances and failures, the performance of the following techniques was analyzed based on the IEC TC10 database: Dornenburg, Duval Triangle, Duval Pentagon, IEC ratio method, Key Gas, and Rogers. The objective of this work was to present relevant information to support students and professionals who work in failure analysis and/or assist in the development of new tools in the DGA field.

Early failure of high-power white LEDs for outdoor applications under extreme electrical stress: Role of silicone encapsulant

Solid state lighting systems have deeply penetrated the market and are widely available. White light emitting diodes (LEDs) are based on a gallium nitride blue-emitting chip and a phosphor-based layer is used to convert blue radiation to white light, in combination with an optical system to enhance light extraction. High temperatures can be detrimental for LED reliability, resulting in a gradual or catastrophic failure. Therefore, the design of such devices, especially in relation to use cases that require operation at high current and temperature levels, needs to be based on reliability considerations.In this work we analyze the robustness of high power white LEDs submitted to driving currents exceeding the nominal levels by means of electrical and optical characterization, as well as on failure analysis techniques. The results highlight a significant degradation of the main optical parameters (luminous flux, color coordinates, correlated color temperature, …). Device cross sectioning highlighted a heavy darkening of the silicone material in the overstressed devices, and a delamination of the phosphors. Raman spectroscopy analysis highlighted an enhancement in luminescence of degraded samples. Such degradation was ascribed to the high temperature reached by the LEDs during the stress, that caused the degradation of the silicone lens; the process can be enhanced by the low efficiency of the phosphors and LEDs at high temperatures, resulting in a stronger heating of the devices.

Analysis and mitigation of errors in external quantum efficiency measurement of solar cells embedded in solar modules

Accurate measurement of external quantum efficiency(EQE) of cells embedded in PV modules is critical for reducing the uncertainty of the flash I-V measurements during secondary calibration of PV modules. This paper presents detailed analysis and quantification of possible sources of errors due to various factors such as shading of target cell, load point determination, variance in cell-to-cell electrical properties and use of AC probe light. Moreover, the technique is also extended for measuring EQE of cells in the degraded modules by changing different cell parameters, thus enabling use of this technique for non-destructive failure analysis. Separate analysis for biasing the module with a programmable load and a four-quadrant power supply is presented. Appropriate correction factors are obtained for accurate load point determination for the two cases with less than 3 % error in the absolute EQE.

Whole-Chip Delayering for Failure Analysis and Quality Assurance

Abstract Broad ion beam delayering is a versatile technique for whole-chip failure analysis. The large area of uniformity coupled with the ability to precisely stop at the layer of interest facilitates repeatable, rapid defect detection anywhere on the chip.

Insight into the Electrochemical Behaviors of NCM811|SiO‐Gr Pouch Battery through Thickness Variation

LiNi0.8Co0.1Mn0.1O2 (NCM811) | SiOx‐graphite (SiO‐Gr.) battery chemistry is of intensive attention because its achievable practical energy density is approaching impressively 300 Wh Kg−1. However, it still suffers rapid capacity fades during repeated cycles, both chemical, electrochemical and mechanical irreversibility contribute. A comprehensive understanding behind the fading behavior of the cell chemistry is required before fully realize the benefits of this chemistry. Herein, the in‐situ thickness variation is introduced as a diagnostic technique and is performed on 5–55 Ah NCM811|SiO‐Gr cells. With the help of Li reference electrode and in‐situ X‐ray diffraction device, the correspondence between thickness variation and the electrode potential is carefully investigated. Firstly, the NCM811|SiO‐Gr cell is characterized with the maximum cell thickness at around 80% state‐of‐charge (SOC) in the discharge process, rather than at 100% SOC. Secondly, the electrochemical behaviors during rate charge/discharge are diagnosed, and a Li platting signal is resolved from thickness variation profile at 2C. This work confirms that the thickness monitoring is a nondestructive and informative complement to conventional diagnostic techniques for failure analysis of pouch cells.

Investigation of Performance Degradation in 600-V HD-GITs Under Power Cycling Tests

Hybrid drain-embedded gate injection transistors (HD-GITs) are one of the most promising technologies for normally-OFF operation. However, the relevant reliability research is relatively lacking. In this article, comprehensive results of degradation performance in 600-V HD-GITs are presented. First, the thermomechanical stress is applied to the device under test (DUT) through an accelerated power cycling test (PCT) setup. Then, thermal and electrical parameters shift under the PCTs are monitored periodically by the PCT setup and the curve tracer. As can be seen from the measurement results, as the PCT progresses, the device not only exhibits package-level degradation, but chip-level degradation also occurs gradually. Notably, leakage current that increases with degradation appears to be a potential choice for lifetime modeling. Finally, the state-of-the-art failure analysis (FA) techniques, such as emission microscopy (EMMI), scanning electron microscope (SEM), focused ion beam (FIB) cutting, and others, are performed. The results of FAs validated the monitoring data, and the root cause of the degradation of the thermal resistance and electrical parameters was eventually confirmed as the solder degradation and the structural damage of the source and gate. Such an investigation can serve as a valuable reference for the reliability of GaN power devices.

Fault tree analysis improvements: A bibliometric analysis and literature review

AbstractFault tree analysis (FTA) is one of the most popular failure analysis techniques that reveal the potential pathways leading to systems or components failure. It has been widely employed in numerous sectors to understand how a system fails and is improved. However, the conventional FTA has been criticized due to a series of inherent shortcomings in the FTA state of the arts. Accordingly, scholars, engineers, and practitioners made their attempts to improve the FTA by dealing with its critical deficiencies over the last decade. However, a few works have been performed to review and synthesize the relevant studies on FTA improvement topics. Thus, the present study is aimed to carry out a systematic literature review of the state‐of‐the‐art theoretical and empirical findings concerning FTA improvement from 2011 to 2021 using the Scopus database collection. In this sense, an in‐depth investigation is conducted using statistical metadata analysis. This subject discusses frontier directions and development trends to reveal the research status. In addition, a bibliometric study is undertaken to ascertain the most productive and influential researchers, research centers, and hotspot fields. It also sheds light on the FTA shortcomings in the existing literature, the evolution in FTA improvement topics, and research opportunities. The outcomes of the present work highlighted that the annual publications on FTA improvement topics are significantly growing, especially after 2019. Besides, Jianxiu Wang, Yan‐Feng Li, and Yihuan Wang are the most productive, prolific, and highly cited authors worldwide; and Asia, particularly China, is the leading contributor in the FTA area. According to Bradford's law, one‐third of all publications (7995) in the field of FTA improvement have been published by 40 sources. Finally, “Decision‐making,” “Risk analysis,” “Uncertainty Analysis,” and “Bayesian Networks” are the four major hot topics integrated into improving the conventional FTA.

A Twofold Clock and Voltage-Based Detection Method for Laser Logic State Imaging Attack

Powerful side-channel analysis (SCA) attacks based on failure analysis (FA) techniques can bypass conventional countermeasures on integrated circuits (ICs) and, therefore, break the entire system’s security. Laser logic state imaging (LLSI) from the IC backside is an example of such attacks, making the contactless probing of static on-die signals possible. Several countermeasures have been proposed to prevent optical probing attacks, such as LLSI. However, these schemes are designed according to the laser properties and its impact on transistors, and hence, they have complex fabrication steps and large area overhead. As a result, they are difficult to verify and implement. In this article, we propose a twofold detection self-timed sensor, which is the first attempt, to our knowledge, for an easy-to-implement circuit-based countermeasure to thwart LLSI attacks. To perform LLSI, the attacker needs to freeze the clock at a point of interest and modulate the voltage supply line at a known frequency to leak the state of transistors through laser light reflections. With these two attack requirements in mind, we design, simulate, and implement clock- and voltage-based sensors that can detect LLSI attacks with very high confidence.

Sample processing by Bi‐FIB for in situ TOF‐SIMS imaging of buried interfaces

Focused ion beam (FIB) is commonly used as a standard machining technique in failure analysis, quality control, reverse engineering, material research, and so on, for the samples having microstructures and nanostructures. FIB combined with time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS), so‐called FIB‐TOF, has attracted attention as a method to determine the three‐dimensional (3D) chemical distributions of complex samples. In general, a highly focused Ga+ ion beam is used for FIB, however, the FIB‐milled area is limited and it was difficult to expand the Ga‐FIB to hundreds of micron length scale for sample fabrication. In order to overcome the drawback, we proposed the Bi‐FIB approach for large scale sample cross‐sectioning. Although the possibility of Bi‐FIB has been reported, there were almost no performance examinations as well as practical applications. In this study, therefore, the authors summarize the comparison of milling rate and milling damage between Ga‐FIB and Bi‐FIB. As a result, it was found that Bi‐FIB can provide a higher milling rate with thinner milling damage. Finally, the Bi‐FIB approach was applied to the interfacial analysis of all‐solid‐state battery (ASSB) material. With this approach, the detailed chemical distributions at the interface were discovered, leading to the better understanding of battery behaviors.

Towards development of an intelligent failure analysis system based on infrared thermography

Electronic components of which reliability cannot be quantified are unacceptable and potentially hazardous, especially in safety-relevant areas such as driver assistance systems and medical technology where the zero-error principle applies. Reliability as a quality criterion has its origin in production, i.e. process variations have a negative influence on the structural integrity of the contact elements on the packaging and interconnect technology and, thus on the device performance in the field under thermo-mechanical load (temperature changes, vibration, humidity). At present, to ensure reproducibility of the reliability of each component, regular quality tests are often carried out in practice. However, a better and reliable approach will carry out 100 % inline checks for traceability and immediate readjustment. This work is the first step towards developing an intelligent non-destructive inline-capable failure analysis technique using infrared thermography. Good data forms the base on which robust and accurate AI algorithms can be trained and developed. However, the obtained thermographic images need to be processed so that subsurface defects can be detected. In this work, prominent algorithms, namely Pulse Phase Thermography (PPT), Thermographic Signal Reconstruction (TSR), Principal Component Thermography (PCT), Slope and Correlation Coefficient, have been thoroughly discussed and examined on the thermographic sequence from a plexiglass sample. A hybrid algorithm of TSR and PCT has also been suggested with promising results. In the end, potential post-processing algorithms from which the obtained results can be used for training an ML/AI model have been discussed.The major problem associated with the deep learning approach is the lack of data in the training set. The model's performance is dependent on the quality and quantity of the training data. The training data should be a good representation of the real-world scenario, i.e. it should be accurate enough and contain enough images, including exceptions for learning reliable features. However, getting sufficient data is a challenge in the manufacturing industry. However, using the Finite Element Model (FEM) approach for data generation can help overcome the data hurdle. In this paper, we also evaluate the potential application of FEM and the problems one faces when trying to generate a large amount of data for training using FEM.

Review of Methodologies for Evaluating Short-Circuit Robustness and Reliability of SiC Power MOSFETs

To accelerate the broad application of silicon carbide (SiC) power MOSFETs, their short-circuit (SC) robustness and reliability must be thoroughly evaluated. This article, therefore, presents an overview of related research methods aiming to meet that objective. Topics reviewed include non-destructive tester (NDT) used to implement SC faults, extraction and analysis of degradation parameters, and failure analysis of SiC MOSFETs. Concerning NDT, its design criteria and methods for evaluating SC robustness are discussed. Then, the key parameters used to assess SC reliability and methods for investigating degradation mechanisms are discussed and summarized. Finally, failure analysis techniques and their contributions to failure mechanisms are reviewed.

Failure assessment of corrosion affected pipeline networks with limited failure data availability

Many pipeline networks around the world do not have a failure history. Because of financial reasons or lack of technology, this is a common issue for asset managers in developing and under-developed countries. In the case of data scarcity, it is not straightforward to assess the risk and reliability of such systems. This research proposes a probabilistic framework for analysing the reliability of corroded pipeline networks with limited failure history availability. To achieve this aim through a comprehensive reliability assessment methodology, state-of-the-art corrosion modeling, component and system failure analysis techniques are utilized. A variety of spatio-temporal reliability assessment methods are employed along with the random-field representation of spatially variable parameters to estimate the failure in a large-scale corrosion affected pipeline network. The applicability of the methodology is evaluated through a case-study of buried pipeline network, and the results are verified with failure history data reported by the industry. The results obtained from the proposed methodology, combined with the cost data provided by asset managers make accurate risk assessment of process/water pipeline possible.

Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems

The real-time application research on the Fuzzy Logic Systems (FLSs) and Artificial Neural Networks (ANN) is vast and, in this paper, a technique for a photovoltaic failure analysis using the type 2 FLS and ANN is proposed. The method is proposed to build T2 FLS with a guaranteed value equal to or lower than T2 and ANN. Several explanations are conducted to illustrate the effectiveness of the methodologies. It is found that both the type 2 Fuzzy and ANN can be configured for productive actions in applications for a PV fault analysis, and choice is typically applied. The methods discussed in this paper lay the groundwork for developing FLSs and ANNs with durable characteristics that will be extremely useful in many functional applications. The result demonstrates that specific fault categories can be detected using the fault identification method, such as damaged PV modules and partial PV unit shades. The average detection performance is similar in both ANN and fuzzy techniques. In comparison, both systems evaluated show approximately the same performance during experiments. The architecture of the type 2 fuzzy logic system and ANN with radial basic function, including the roles of the output port and the rules for identifying the type of defect in the PV structure is slightly different.

Improved Focused Ion Beam Sample Preparation Techniques for Transmission Electron Microscopy and Failure Analysis of Memristor Devices

Improved Focused Ion Beam Sample Preparation Techniques for Transmission Electron Microscopy and Failure Analysis of Memristor Devices

Advanced Physical Failure Analysis Techniques for Rescuing Damaged Samples with Cracks, Scratches, or Unevenness in Delayering

Advanced Physical Failure Analysis Techniques for Rescuing Damaged Samples with Cracks, Scratches, or Unevenness in Delayering

Hybrid adaptive model to optimise components replacement strategy: A case study of railway brake blocks failure analysis

In this paper, we propose a novel hybrid adaptive model (HAM) that integrates Gaussian mixture probabilistic machine learning (ML), Weibull time-to-failure feature, and value of information (VOI) techniques for complex engineering failure analysis. The objective is to establish an optimum components replacement intervention strategy for composite brake blocks of railway rolling stocks to better curtail failures and possible accidents. The HAM considers brake blocks as rudimentary systems for emergency brake application to prevent catastrophic rail accidents under nonlinear and dynamic environmental conditions. A Gaussian mixture regression technique with a rational quadratic kernel function is used to develop a predictive wear model that applies wear measurement as training data. The Weibull feature estimates the threshold maintenance replacement strategy pertaining to other premature failure modes before the brake blocks’ legal scrapping wear limit is reached. Finally, the VOI feature establishes the net loss in terms of cost for the proposed replacement options to guide optimum component replacement selection strategy under organisational resource constraints. Based on operational data obtained from several brake blocks of the London Underground Trains fleet, the proposed HAM failure analysis technique provided a better balance between safety and cost-effectiveness compared to other popular ML approaches.

Practical Demonstration of a Hybrid Model for Optimising the Reliability, Risk, and Maintenance of Rolling Stock Subsystem

Railway transport system (RTS) failures exert enormous strain on end-users and operators owing to in-service reliability failure. Despite the extensive research on improving the reliability of RTS, such as signalling, tracks, and infrastructure, few attempts have been made to develop an effective optimisation model for improving the reliability, and maintenance of rolling stock subsystems. In this paper, a new hybrid model that integrates reliability, risk, and maintenance techniques is proposed to facilitate engineering failure and asset management decision analysis. The upstream segment of the model consists of risk and reliability techniques for bottom-up and top-down failure analysis using failure mode effects and criticality analysis and fault tree analysis, respectively. The downstream segment consists of a (1) decision-making grid (DMG) for the appropriate allocation of maintenance strategies using a decision map and (2) group decision-making analysis for selecting appropriate improvement options for subsystems allocated to the worst region of the DMG map using the multi-criteria pairwise comparison features of the analytical hierarchy process. The hybrid model was illustrated through a case study for replacing an unreliable pneumatic brake unit (PBU) using operational data from a UK-based train operator where the frequency of failures and delay minutes exceeded the operator’s original target by 300% and 900%, respectively. The results indicate that the novel hybrid model can effectively analyse and identify a new PBU subsystem that meets the operator’s reliability, risk, and maintenance requirements.

Integration of simulation and Markov Chains to support Bayesian Networks for probabilistic failure analysis of complex systems

Development of failure analysis techniques for complex engineering systems is evolving rapidly. Complexity in these systems refers to the complex interrelations among system components, variables, factors, and parameters as well as the large number of components to include in the study. It is not an easy task to include all interrelationships of a complex system into one representation. New dynamic and uncertain factors affecting engineering systems, like climate change, new technologies, and new uses, make it clear that the water reservoir systems operations and performance are under probabilistic inputs from many different factors. This means that failure of such systems should be assessed using multidisciplinary probabilistic uncertainty measures. Bayesian Networks (BNs) provide a flexible way of representing such complex systems and their interrelating components probabilistically and in a single unified representation. Compared to other techniques such as fault tree and event tree analyses methods, BN is useful in representing complex networks that have multiple events and different types of variables in one representation, with the ability to predict the effects, or diagnose the causes leading to a certain effect. In this paper, two proposed methodologies are developed to support BNs in dealing with the failure analysis of complex engineering systems, i.e. Simulation Supported Bayesian Networks (SSBNs), and Markov Chain Simulation Supported Bayesian Networks (MCSSBNs). For complex networks, whose failures are affected by a large number of uncertain interconnected variables, these proposed methods are used for efficiently predicting failure probabilities. Compared to exhaustive simulation, the new tools have the distinction of decomposing the complex system into many sub-systems, which makes it easier for understanding the network and faster for simulating the entire network while taking multiple operation scenarios into consideration. The efficiency of these techniques is demonstrated through their application to a pilot system of two dam reservoirs, where the results of SSBNs and MCSSBNs are compared with those of the simulation of entire system operations.