Failure Mode Clustering is Electronic Assemblies Using Sammon’s Mapping With Supervised Training of Perceptrons

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Prashant Gupta ◽  
Kai Goebel
Keyword(s):  
Failure Mode ◽  
Solder Ball ◽  
Failure Modes ◽  
Feature Vector ◽  
Feature Space ◽  
Image Correlation ◽  
Neural Net ◽  
Sammon’S Mapping ◽  
Electronic Assemblies ◽  
Diagnostic Detection

An anomaly detection and failure mode classification method has been developed for electronic assemblies with multiple failure modes. The presented prognostic health management method targets the pre-failure space of the electronic assembly life to trigger repair or replacement of impending failures. Presently, health monitoring systems focus on reactive diagnostic detection of failure modes. Examples of diagnostic detection include the built in self test and on-board diagnostics. In this paper, damage pre-cursors from time-spectral measurements of the electronic assemblies has been measured under applied vibration and shock stimulus. The time-evolution of spectral content of the damage pre-cursors has been studied using joint time frequency analysis in a full-field manner on the printed circuit assembly. Frequency moments have been used to build a feature vector. Evolution of the feature vector with damage initiation and progression has been studied under shock and vibration. The feature vector from multiple locations in the board assemblies has been mapped into a de-correlated feature space using Sammon’s mapping. Several chip-scale packages have been studied, with SAC305 and SAC405 leadfree second-level interconnects. Transient strain has been measured during the drop-event using digital image correlation and high-speed cameras operating at 100,000 fps. Continuity has been monitored simultaneously for failure identification. In addition, explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, trace fracture, package falloff and solder ball failure. The neural net has been trained using simulated data-sets created from error-seeded models with specific failure modes. The neural net has then been used to identify and classify the failure modes in board assemblies experimentally. Supervised learning of multilayer neural net in conjunction with parity has been used to identify the hard-separation boundaries between failure mode clusters in the de-correlated feature space. The assemblies have been cross-sectioned to verify the identified failure modes. Cross-sections indicate that the experimentally measured failures modes correlate well with the position of the cluster in the de-correlated feature space.

Feature Vector Based Failure Mode Identification and Prognostication of Electronics Subjected to Shock and Vibration

2009 ◽  
Author(s):  
Pradeep Lall ◽  
Prashant Gupta ◽  
Arjun Angral ◽  
Jeff Suhling
Keyword(s):  
Experimental Data ◽  
Failure Mode ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Solder Ball ◽  
Failure Modes ◽  
Feature Space ◽  
Time Frequency ◽  
Fault Mode ◽  
Electronic Assemblies

Failures in electronics subjected to shock and vibration are typically diagnosed using the built-in self test (BIST) or using continuity monitoring of daisy-chained packages. The BIST which is extensively used for diagnostics or identification of failure, is focused on reactive failure detection and provides limited insight into reliability and residual life. In this paper, a new technique has been developed for health monitoring and failure mode classification based on measured damage precursors. A feature extraction technique in the joint-time frequency domain has been developed along with pattern classifiers for fault diagnosis of electronics at product-level. The Karhunen Loe´ve transform (KLT) has been used for feature reduction and de-correlation of the feature vectors for fault mode classification in electronic assemblies. Euclidean, and Mahalanobis, and Bayesian distance classifiers based on joint-time frequency analysis, have been used for classification of the resulting feature space. Previously, the authors have developed damage pre-cursors based on time and spectral techniques for health monitoring of electronics without reliance on continuity data from daisy-chained packages. Statistical Pattern Recognition techniques based on wavelet packet energy decomposition [Lall 2006a] have been studied by authors for quantification of shock damage in electronic assemblies, and auto-regressive moving average, and time-frequency techniques have been investigated for system identification, condition monitoring, and fault detection and diagnosis in electronic systems [Lall 2008]. However, identification of specific failure modes was not possible. In this paper, various fault modes such as solder inter-connect failure, inter-connect missing, chip delamination chip cracking etc in various packaging architectures have been classified using clustering of feature vectors based on the KLT approach [Goumas 2002]. The KLT de-correlates the feature space and identifies dominant directions to describe the space, eliminating directions that encode little useful information about the features [Qian 1996, Schalkoff 1972, Theodoridis 1998, Tou 1974]. The clustered damage pre-cursors have been correlated with underlying damage. Several chip-scale packages have been studied, with leadfree second-level interconnects including SAC105, SAC305 alloys. Transient strain has been measured during the drop-event using digital image correlation and high-speed cameras operating at 100,000 fps. Continuity has been monitored simultaneously for failure identification. Fault-mode classification has been done using KLT and joint-time-frequency analysis of the experimental data. In addition, explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, trace fracture, package falloff and solder ball failure. Models using cohesive elements present at the solder joint-copper pad interface at both the PCB and package side have also been created to study the traction-separation behavior of solder. Fault modes predicted by simulation based pre-cursors have been correlated with those from experimental data.

Anomaly-Detection and Prognostication of Electronics Subjected to Shock and Vibration

2009 ◽  
Author(s):  
Pradeep Lall ◽  
Prashant Gupta ◽  
Arjun Angral ◽  
Jeff Suhling
Keyword(s):  
Experimental Data ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Solder Ball ◽  
Failure Modes ◽  
Feature Space ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Fault Mode ◽  
Electronic Assemblies

Failures in electronics subjected to shock and vibration are typically diagnosed using the built-in self test (BIST) or using continuity monitoring of daisy-chained packages. The BIST which is extensively used for diagnostics or identification of failure, is focused on reactive failure detection and provides limited insight into reliability and residual life. In this paper, a new technique has been developed for health monitoring and failure mode classification based on measured damage precursors. A feature extraction technique in the joint-time frequency domain has been developed along with pattern classifiers for fault diagnosis of electronics at product-level. The Karhunen Loe´ve transform (KLT) has been used for feature reduction and de-correlation of the feature vectors for fault mode classification in electronic assemblies. Euclidean, and Mahalanobis, and Bayesian distance classifiers based on joint-time frequency analysis, have been used for classification of the resulting feature space. Previously, the authors have developed damage pre-cursors based on time and spectral techniques for health monitoring of electronics without reliance on continuity data from daisy-chained packages. Statistical Pattern Recognition techniques based on wavelet packet energy decomposition [Lall 2006a] have been studied by authors for quantification of shock damage in electronic assemblies, and auto-regressive moving average, and time-frequency techniques have been investigated for system identification, condition monitoring, and fault detection and diagnosis in electronic systems [Lall 2008]. However, identification of specific failure modes was not possible. In this paper, various fault modes such as solder inter-connect failure, inter-connect missing, chip delamination chip cracking etc in various packaging architectures have been classified using clustering of feature vectors based on the KLT approach [Goumas 2002]. The KLT de-correlates the feature space and identifies dominant directions to describe the space, eliminating directions that encode little useful information about the features [Qian 1996, Schalkoff 1972, Theodoridis 1998, Tou 1974]. The clustered damage pre-cursors have been correlated with underlying damage. Several chip-scale packages have been studied, with leadfree second-level interconnects including SAC105, SAC305 alloys. Transient strain has been measured during the drop-event using digital image correlation and high-speed cameras operating at 100,000 fps. Continuity has been monitored simultaneously for failure identification. Fault-mode classification has been done using KLT and joint-time-frequency analysis of the experimental data. In addition, explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, trace fracture, package falloff and solder ball failure. Models using cohesive elements present at the solder joint-copper pad interface at both the PCB and package side have also been created to study the traction-separation behavior of solder. Fault modes predicted by simulation based pre-cursors have been correlated with those from experimental data.

KL Transform and Neural-Net Based Framework for Failure Modes Classification in Electronics Subjected to Mechanical-Shock

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Prashant Gupta ◽  
Kai Goebel
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Feature Space ◽  
System Level ◽  
Neural Net ◽  
Mechanical Shock ◽  
Time Frequency ◽  
Damage Initiation

Electronic systems under extreme shock and vibration environments including shock and vibration may sustain several failure modes simultaneously. Previous experience of the authors indicates that the dominant failure modes experienced by packages in a drop and shock frame work are in the solder interconnects including cracks at the package and the board interface, pad cratering, copper trace fatigue, and bulk-failure in the solder joint. In this paper, a method has been presented for failure mode classification using a combination of Karhunen Loe´ve transform with parity-based stepwise supervised training of a perceptrons. Early classification of multiple failure modes in the pre-failure space using supervised neural networks in conjunction with Karhunen Loe´ve transform is new. Feature space has been formed by joint time frequency analysis. Since the cumulative damage may be accrued under repetitive loading with exposure to multiple shock events, the area array assemblies have been exposed to shock and feature vectors constructed to track damage initiation and progression. Error Back propagation learning algorithm has been used for stepwise parity of each particular failure mode. The classified failure modes and failure regions belonging to each particular failure modes in the feature space are also validated by simulation of the designed neural network used for parity of feature space. Statistical similarity and validation of different classified dominant failure modes is performed by multivariate analysis of variance and Hoteling’s T-square. The results of different classified dominant failure modes are also correlated with the experimental cross sections of the failed test assemblies. The methodology adopted in this paper can perform real-time fault monitoring with identification of specific dominant failure mode and is scalable to system level reliability.

Failure-Mode Clustering for Electronics Under Shock and Vibration Using Neural-Net Based Self-Organized Maps

2010 ◽  
Author(s):  
Pradeep Lall ◽  
Prashant Gupta
Keyword(s):  
High Speed ◽  
Solder Ball ◽  
Failure Modes ◽  
Remaining Useful Life ◽  
Image Correlation ◽  
Electronic Systems ◽  
Neural Net ◽  
Multiple Failure Modes ◽  
Explicit Finite Element ◽  
Damage Initiation

In this paper prognostic framework for electronic systems has been developed with neural network based self organizing maps for clustering of failure modes. The presented approach resides in the pre-failure space with a focus on electronic systems with multiple failure modes. Portable electronic products subjected to transient shock may exhibit multiple failure modes in vicinity of the interconnects. Failure is often diagnosed by loss of functionality using techniques including the built-in self test, which provides limited insight into reliability and remaining useful life. Unsupervised learning of the neural net has been used to train the neural net for identification of individual failure modes. Feature vectors have been developed based on damage pre-cursors from time-spectral measurements. The clustered damage pre-cursors have been correlated with failure modes of the underlying damage. Several chip-scale packages have been studied, with leadfree second-level interconnects including SAC305, SAC405 alloys. Transient strain has been measured during the drop-event using digital image correlation and high-speed cameras operating at 100,000 fps. Continuity has been monitored simultaneously for failure identification. In addition, explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, trace fracture, package falloff and solder ball failure. Fault modes predicted by simulation based pre-cursors have been correlated with those from experimental data. Activation of different neurons in the lattice for various failure modes and combinations of failure modes has been demonstrated. Previously the authors have developed techniques based on statistical pattern recognition for leading indication of impending failure and detection of damage initiation and progression [Lall 2006a, 2007a, 2008]. Early classification of multiple failure modes in the pre-failure space is new.

scholarly journals Weakening Investigation of Reservoir Rock by Coupled Uniaxial Compression, Computed Tomography and Digital Image Correlation Methods: A Case Study

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 344
Author(s):  
Peiwu Shen ◽  
Huiming Tang ◽  
Bocheng Zhang ◽  
Yibing Ning ◽  
Xuexue Su ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Digital Image Correlation ◽  
Failure Mode ◽  
Digital Image ◽  
Failure Modes ◽  
Image Correlation ◽  
Reservoir Rock ◽  
Wetting And Drying ◽  
Structure Variation ◽  
Reservoir Area

Cyclic wetting and drying treatment is commonly used to accelerate the weakening process of reservoir rock. The weakening is reflected in strength variation and structure variation, while the latter receives less attention. Based on a series of cyclic wetting and drying tests, this study tentatively applied the uniaxial compressive test, computed tomography (CT) test and digital image correlation (DIC) test to investigate the weakening of slate in a reservoir area. Test results show that the weakening is mainly reflected in the reduction of compressive strength, followed by the decrease of ability to resist cracking and elastic deformation. The weakening seems more likely to be caused by structure variation rather than composition change. Two failure modes, e.g., splitting and splitting-tension, are concluded based on the crack paths: the splitting failure mode occurs in the highly weathered samples and the splitting-tension failure mode appears in the low-weathered samples. The transition zones of deformation are inside samples. The nephogram maps quantify the continuous deformation and correspond to the aforementioned structure variation process. This study offers comprehensive methods to the weakening investigation of slate in reservoir area and may provide qualitative reference in the stability evaluation of related slate rock slope.

Combine Nanoprobing Technique with Difference Analysis to Identify Nonvisual Failures

2006 ◽  
Author(s):  
Cha-Ming Shen ◽  
Tsan-Cheng Chuang ◽  
Jie-Fei Chang ◽  
Jin-Hong Chou
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Electrical Characteristic ◽  
The Novel ◽  
Difference Analysis ◽  
Original Idea ◽  
Deductive Method ◽  
Complete Measurement

Abstract This paper presents a novel deductive methodology, which is accomplished by applying difference analysis to nano-probing technique. In order to prove the novel methodology, the specimens with 90nm process and soft failures were chosen for the experiment. The objective is to overcome the difficulty in detecting non-visual, erratic, and complex failure modes. And the original idea of this deductive method is based on the complete measurement of electrical characteristic by nano-probing and difference analysis. The capability to distinguish erratic and invisible defect was proven, even when the compound and complicated failure mode resulted in a puzzling characteristic.

Case Study and Fault Modeling for Wrong Redundancy Evaluation on DRAM Devices

2007 ◽  
Author(s):  
Martin Versen ◽  
Dorina Diaconescu ◽  
Jerome Touzel
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Fault Modeling ◽  
Mode Analysis ◽  
Root Cause ◽  
Failure Mode Analysis

Abstract The characterization of failure modes of DRAM is often straight forward if array related hard failures with specific addresses for localization are concerned. The paper presents a case study of a bitline oriented failure mode connected to a redundancy evaluation in the DRAM periphery. The failure mode analysis and fault modeling focus both on the root-cause and on the test aspects of the problem.

Methodology for Analysis of Schottky Diode Failures

2010 ◽  
Author(s):  
Bhanu P. Sood ◽  
Michael Pecht ◽  
John Miker ◽  
Tom Wanek
Keyword(s):  
Failure Mode ◽  
Schottky Diode ◽  
Failure Modes ◽  
Schottky Diodes ◽  
Failure Mechanisms ◽  
Forward Voltage ◽  
Fast Switching ◽  
Voltage Drops ◽  
The Common

Abstract Schottky diodes are semiconductor switching devices with low forward voltage drops and very fast switching speeds. This paper provides an overview of the common failure modes in Schottky diodes and corresponding failure mechanisms associated with each failure mode. Results of material level evaluation on diodes and packages as well as manufacturing and assembly processes are analyzed to identify a set of possible failure sites with associated failure modes, mechanisms, and causes. A case study is then presented to illustrate the application of a systematic FMMEA methodology to the analysis of a specific failure in a Schottky diode package.

The Effect of Ball Pad Metallurgy and Ball Composition on Solder Ball Integrity of Plastic Ball Grid Array Packages

1998 ◽  
Author(s):  
C.H. Zhong ◽  
Sung Yi
Keyword(s):  
Failure Mode ◽  
Barrier Layer ◽  
Shear Force ◽  
Solder Ball ◽  
Ball Grid Array ◽  
Reliability Test ◽  
Shear Forces ◽  
Ball Shear ◽  
Plastic Ball ◽  
Plastic Ball Grid Array

Abstract Ball shear forces of plastic ball grid array (PBGA) packages are found to decrease after reliability test. Packages with different ball pad metallurgy form different intermetallic compounds (IMC) thus ball shear forces and failure modes are different. The characteristic and dynamic process of IMC formed are decided by ball pad metallurgy which includes Ni barrier layer and Au layer thickness. Solder ball composition also affects IMC formation dynamic process. There is basically no difference in ball shear force and failure mode for packages with different under ball pad metallurgy before reliability test. However shear force decreased and failure mode changed after reliability test, especially when packages exposed to high temperature. Major difference in ball shear force and failure mode was found for ball pad metallurgy of Ni barrier layer including Ni-P, pure Ni and Ni-Co. Solder ball composition was found to affect the IMC formation rate.

Failure mode and effect analysis (FMEA) to improve collaborative project-based learning: Case study of a Study and Research Path in mechanical engineering

2021 ◽  
pp. 030641902199904
Author(s):  
Elena Bartolomé ◽  
Paula Benítez
Keyword(s):  
Active Learning ◽  
Failure Mode ◽  
Mechanical Engineering ◽  
Failure Modes ◽  
Collaborative Study ◽  
Project Based Learning ◽  
Educational Process ◽  
Effect Analysis ◽  
Questions And Answers

Failure Mode and Effect Analysis (FMEA) is a powerful quality tool, widely used in industry, for the identification of failure modes, their effects and causes. In this work, we investigated the utility of FMEA in the education field to improve active learning processes. In our case study, the FMEA principles were adapted to assess the risk of failures in a Mechanical Engineering course on “Theory of Machines and Mechanisms” conducted through a project-based, collaborative “Study and Research Path (SRP)” methodology. The SRP is an active learning instruction format which is initiated by a generating question that leads to a sequence of derived questions and answers, and combines moments of study and inquiry. By applying the FMEA, the teaching team was able to identify the most critical failures of the process, and implement corrective actions to improve the SRP in the subsequent year. Thus, our work shows that FMEA represents a simple tool of risk assesment which can serve to identify criticality in educational process, and improve the quality of active learning.

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