Prognosis Methodologies for Health Management of Electronics and MEMS Packaging

2004 ◽  
Author(s):  
Pradeep Lall ◽  
Nokibul Islam ◽  
Kaysar Rahim ◽  
Jeff Suhling
Keyword(s):  
Printed Circuit Board ◽  
Prediction Models ◽  
Health Management ◽  
Residual Life ◽  
Structural Evolution ◽  
Remaining Useful Life ◽  
Circuit Board ◽  
Leading Indicators ◽  
Phase Size ◽  
Material State

The current state-of-art in managing system reliability is geared towards the development of life-prediction models for unaged pristine materials under known loading conditions based on relationships such as the Paris’s Power Law [Paris, et. al 1960, 1961], Coffin-Manson Relationship [Coffin 1954; Tavernelli, et. al. 1959; Smith, et. al. 1964; Manson, et. al. 1964] and the S-N Diagram. There is need for methods and processes which will allow interrogation of complex systems and sub-systems to determine the remaining useful life prior to repair or replacement. This capability of determination of material or system state is called “prognosis”. In this paper, a methodology for prognosis-of-electronics has been demonstrated with data of leading indicators of failure for accurate assessment of product damage significantly prior to appearance of any macro-indicators of damage. Proxies for leading indicators of failure have been developed including – micro-structural evolution characterized by average phase size and interfacial stresses at interface of silicon structures. Structures examined include – electronics package, MEMS Packages and interconnections on a metal backed printed circuit board typical of electronics deployed in harsh environments. Since, an aged material knows its state the research presented in this paper focuses on enhancing the understanding of material damage to facilitate proper interrogation of material state. Mathematical relationship has been developed between phase growth rate and time-to-1-percent failure to enable the computation of damage manifested and a forward estimate of residual life.

Comparison of Prognostic Health Management Algorithms for Assessment of Electronic Interconnect Reliability Under Vibration

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Pradeep Lall ◽  
Ryan Lowe
Keyword(s):  
Kalman Filter ◽  
Printed Circuit Board ◽  
Health Management ◽  
Remaining Useful Life ◽  
Circuit Board ◽  
Data Set ◽  
Printed Circuit ◽  
Interconnect Reliability ◽  
Useful Life ◽  
Management Algorithms

This paper compares three prognostic algorithms applied to the same data recorded during the failure of a solder joint in ball grid array component attached to a printed circuit board. The objective is to expand on the relative strengths and weaknesses of each proposed algorithm. Emphasis will be placed on highlighting differences in underlying assumptions required for each algorithm, details of remaining useful life calculations, and methods of uncertainty quantification. Metrics tailored specifically for prognostic health monitoring (PHM) are presented to characterize the performance of predictions. The relative merits of PHM algorithms based on a Kalman filter, extended Kalman filter, and a particle filter all demonstrated on the same data set will be discussed. The paper concludes by discussing which algorithm performs best given the information available about the system being monitored.

Comparison of Prognostic Health Management Algorithms for Assessment of Electronic Interconnect Reliability

2013 ◽  
Author(s):  
Pradeep Lall ◽  
Ryan Lowe ◽  
Kai Goebel
Keyword(s):  
Kalman Filter ◽  
Printed Circuit Board ◽  
Health Management ◽  
Remaining Useful Life ◽  
Circuit Board ◽  
Data Set ◽  
Printed Circuit ◽  
Interconnect Reliability ◽  
Useful Life ◽  
Management Algorithms

This paper compares three prognostic algorithms applied to the same data recorded during the failure of a solder joint in ball grid array component attached to a printed circuit board. The objective is to expand on the relative strengths and weaknesses of each proposed algorithm. Emphasis will be placed on highlighting differences in underlying assumptions required for each algorithm, details of remaining useful life calculations, and methods of uncertainty quantification. Metrics tailored specifically for Prognostic Health Monitoring (PHM) are presented to characterize the performance of predictions. The relative merits of PHM algorithms based on a Kalman filter, extended Kalman filter, and a particle filter all demonstrated on the same data set will be discussed. The paper concludes by discussing which algorithm performs best given the information available about the system being monitored.

Leading Indicators of Failure for Prognostication of Leaded and Lead-Free Electronics in Harsh Environments

2005 ◽  
Author(s):  
Pradeep Lall ◽  
Nokibul Islam ◽  
Prakriti Choudhary ◽  
Jeff Suhling
Keyword(s):  
Residual Life ◽  
Prognostic Indicators ◽  
Leading Indicators ◽  
Damage State ◽  
Solder Interconnects ◽  
Damage Progression ◽  
Plastic Ball ◽  
Phase Size ◽  
Electronic Assemblies

In this paper, a methodology for prognostication-of-electronics has been developed for accurate assessment of residual life in a deployed electronic components, and determination of damage-state in absence of macro-indicators of failure. Proxies for leading indicators-of-failure have been identified and correlated with damage progression under thermomechanical loads. Examples of proxies include — microstructural evolution characterized by average phase size and intermetallic growth rate in solder interconnects. Validity of damage proxies has been investigated for both 63Sn37Pb leaded and SnAgCu leadfree electronics. Structures examined include — plastic ball grid array format electronic and MEMS Packages and discrete devices assembled with FR4-06 laminates. Focus of the research presented in this paper is on interrogation of the aged material’s damage state and enhancing the understanding of damage progression. The research is aimed at development of damage relationships for determination of residual life of aged electronics and assessment of design margins instead of life prediction of new components. The prognostic indicators presented in this paper, can be used for health monitoring of electronic assemblies.

scholarly journals Data Mining for Material Feeding Optimization of Printed Circuit Board Template Production

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Shengping Lv ◽  
Binbin Zheng ◽  
Hoyeol Kim ◽  
Qiangsheng Yue
Keyword(s):  
Data Mining ◽  
Structural Change ◽  
Printed Circuit Board ◽  
Prediction Models ◽  
Raw Material ◽  
Circuit Board ◽  
Production Logistics ◽  
Printed Circuit ◽  
Data Mining Approach ◽  
Scrap Rate

Improving the accuracy of material feeding for printed circuit board (PCB) template orders can reduce the overall cost for factories. In this paper, a data mining approach based on multivariate boxplot, multiple structural change model (MSCM), neighborhood component feature selection (NCFS), and artificial neural networks (ANN) was developed for the prediction of scrap rate and material feeding optimization. Scrap rate related variables were specified and 30,117 samples of the orders were exported from a PCB template production company. Multivariate boxplot was developed for outlier detection. MSCM was employed to explore the structural change of the samples that were finally partitioned into six groups. NCFS and ANN were utilized to select scrap rate related features and construct prediction models for each group of the samples, respectively. Performances of the proposed model were compared to manual feeding, ANN, and the results indicate that the approach exhibits obvious superiority to the other two methods by reducing surplus rate and supplemental feeding rate simultaneously and thereby reduces the comprehensive cost of raw material, production, logistics, inventory, disposal, and delivery tardiness compensation.

Feasibility of PCB-Integrated Vibration Sensors for Condition Monitoring of Electronic Systems

2018 ◽  
Author(s):  
Klas Brinkfeldt ◽  
Göran Wetter ◽  
Andreas Lövberg ◽  
Per-Erik Tegehall ◽  
Dag Andersson ◽  
...  
Keyword(s):  
Printed Circuit Board ◽  
Health Management ◽  
Circuit Board ◽  
Piezoelectric Sensors ◽  
Sensor Data ◽  
Reflow Soldering ◽  
Physics Of Failure ◽  
Sensor Material ◽  
Soldering Process ◽  
Failure Models

The increasing complexity of electronics in systems used in safety critical applications, such as for example self-driving vehicles requires new methods to assure the hardware reliability of the electronic assemblies. Prognostics and Health Management (PHM) that uses a combination of data-driven and Physics-of-Failure models is a promising approach to avoid unexpected failures in the field. However, to enable PHM based partly on Physics-of-Failure models, sensor data that measures the relevant environment loads to which the electronics is subjected during its mission life are required. In this work, the feasibility to manufacture and use integrated sensors in the inner layers of a printed circuit board (PCB) as mission load indicators measuring impacts and vibrations has been investigated. A four-layered PCB was designed in which piezoelectric sensors based on polyvinylidenefluoride-co-trifluoroethylene (PVDF-TrFE) were printed on one of the laminate layers before the lamination process. Manufacturing of the PCB was followed by the assembly of components consisting of BGAs and QFN packages in a standard production reflow soldering process. Tests to ensure that the functionality of the sensor material was unaffected by the soldering process were performed. Results showed a yield of approximately 30% of the sensors after the reflow soldering process. The yield was also dependent on sensor placement and possibly shape. Optimization of the sensor design and placement is expected to bring the yield to 50 % or better. The sensors responded as expected to impact tests. Delamination areas were present in the test PCBs, which requires further investigation. The delamination does not seem to be due to the presence of embedded sensors alone but rather the result of a combination of several factors. The conclusion of this work is that it is feasible to embed piezoelectric sensors in the layers of a PCB.

PHM of Leadfree Electronic Assemblies Using Resistance Spectroscopy With Sequential Importance Resampling

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Ryan Lowe ◽  
Kai Goebel
Keyword(s):  
State Space ◽  
Feature Vector ◽  
Health Management ◽  
Residual Life ◽  
The State ◽  
Remaining Useful Life ◽  
Damage Initiation ◽  
State Variable ◽  
Useful Life ◽  
Electronic Assemblies

Electronic assemblies have been monitored using state-space vectors from resistance spectroscopy, phase-sensitive detection and particle filtering (PF) to quantify damage initiation, progression and remaining useful life of the electronic assembly. A prognostication health management (PHM) methodology has been presented for electronic components subjected to mechanical shock and vibration. The presented methodology is an advancement of the state-of-art, which presently focuses on reactive failure detection and provides limited or no insight into the system reliability and residual life. Previously damage initiation, damage progression, and residual life in the pre-failure space has been correlated with micro-structural damage based proxies, feature vectors based on time, spectral and joint time-frequency characteristics of electronics [Lall2004a–d, 2005a–b, 2006a–f, 2007a–e, 2008a–f]. Precise resistance measurements based on the resistance spectroscopy method have been used to monitor interconnects for damage and prognosticate failure [Lall 2009a,b, 2010a,b, Constable 1992, 2001]. In this paper, the effectiveness of the proposed particle filter and resistance spectroscopy based approach in a prognostic health management (PHM) framework has been demonstrated for electronics. The measured state variable has been related to the underlying damage state using non-linear finite element analysis. The particle filter has been used to estimate the state variable, rate of change of the state variable, acceleration of the state variable and construct a feature vector. The estimated state-space parameters have been used to extrapolate the feature vector into the future and predict the time-to-failure at which the feature vector will cross the failure threshold. Remaining useful life has been calculated based on the evolution of the state space feature vector. Standard prognostic health management metrics were used to quantify the performance of the algorithm against the actual remaining useful life. Application to part replacement decisions for ultra-high reliability system has been demonstrated. Using the technique described in the paper the appropriate time to reorder a replacement part could be monitored, and defended statistically. Robustness of the prognostication algorithm has been quantified using standard performance evaluation metrics.

PHM of Leadfree Interconnects Using Resistance Spectroscopy Based Particle Filter Models for Shock and Vibration Environments

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Ryan Lowe ◽  
Kai Goebel
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Feature Vector ◽  
Health Management ◽  
Residual Life ◽  
The State ◽  
Remaining Useful Life ◽  
Damage Initiation ◽  
State Variable ◽  
Useful Life

Electronic assemblies have been monitored using state-space vectors from resistance spectroscopy, phase-sensitive detection and particle filtering (PF) to quantify damage initiation, progression and remaining useful life of the electronic assembly. A prognostication health management (PHM) methodology has been presented for electronic components subjected to mechanical shock and vibration. The presented methodology is an advancement of the state-of-art, which presently focuses on reactive failure detection and provides limited or no insight into the system reliability and residual life. Previously damage initiation, damage progression, and residual life in the pre-failure space has been correlated with micro-structural damage based proxies, feature vectors based on time, spectral and joint time-frequency characteristics of electronics [Lall2004a-d, 2005a-b, 2006a-f, 2007a-e, 2008a-f]. Precise resistance measurements based on the resistance spectroscopy method have been used to monitor interconnects for damage and prognosticate failure [Lall 2009a,b, 2010a,b, Constable 1992, 2001]. In this paper, the effectiveness of the proposed particle filter and resistance spectroscopy based approach in a prognostic health management (PHM) framework has been demonstrated for electronics. The measured state variable has been related to the underlying damage state using non-linear finite element analysis. The particle filter has been used to estimate the state variable, rate of change of the state variable, acceleration of the state variable and construct a feature vector. The estimated state-space parameters have been used to extrapolate the feature vector into the future and predict the time-to-failure at which the feature vector will cross the failure threshold. Remaining useful life has been calculated based on the evolution of the state space feature vector. Standard prognostic health management metrics were used to quantify the performance of the algorithm against the actual remaining useful life. Application to part replacement decisions for ultra-high reliability system has been demonstrated. Using the technique described in the paper the appropriate time to reorder a replacement part could be monitored, and defended statistically. Robustness of the prognostication algorithm has been quantified using standard performance evaluation metrics.

Methodologies for Prognostication and Health Monitoring of Leaded and Lead Free Electronics and MEMS Packages in Harsh Environments

2005 ◽  
Author(s):  
Pradeep Lall ◽  
Nokibul Islam ◽  
Prakriti Choudhary ◽  
Jeff Suhling
Keyword(s):  
Health Monitoring ◽  
Residual Life ◽  
Structural Evolution ◽  
Prognostic Indicators ◽  
Damage State ◽  
Solder Interconnects ◽  
Damage Progression ◽  
Plastic Ball ◽  
Phase Size

In this paper, a methodology for prognostication-of-electronics has been developed for accurate assessment of residual life in a deployed electronic components, and determination of damage-state in absence of macro-indicators of failure. Proxies for leading indicators-of-failure have been identified and correlated with damage progression under thermo-mechanical loads. Examples of proxies include — micro-structural evolution characterized by average phase size and intermetallic growth rate in solder interconnects. Validity of damage proxies has been investigated for both 63Sn37Pb leaded and SnAgCu leadfree electronics. Structures examined include — plastic ball grid array format electronic and MEMS Packages and discrete devices assembled with FR4-06 laminates. Focus of the research presented in this paper is on interrogation of the aged material’s damage state and enhancing the understanding of damage progression. The research is aimed at development of damage relationships for determination of residual life of aged electronics and assessment of design margins instead of life prediction of new components. The prognostic indicators presented in this paper, can be used for health monitoring of electronic assemblies.

Leading Prognostic Indicators for Health Management of Electronics Under Thermo-Mechanical Stresses

2007 ◽  
Author(s):  
Pradeep Lall ◽  
Madhura Hande ◽  
Chandan Bhat ◽  
Jeff Suhling
Keyword(s):  
Health Monitoring ◽  
Least Squares Method ◽  
Health Management ◽  
Residual Life ◽  
Electronic System ◽  
Prognostic Indicators ◽  
Minimal Risk ◽  
Leading Indicators ◽  
Mechanical Loads ◽  
Marquardt Algorithm

Methodologies for prognostication and health monitoring can significantly impact electronic reliability for applications in which even minimal risk of failure may be unbearable. Presently, health monitoring approaches such as the built-in self-test (BIST) are based on reactive failure diagnostics and unable to determine residual-life or estimate residual-reliability [Allen 2003, Drees 2004, Gao 2002, Rosenthal 1990]. Prognostics health-monitoring (PHM) approach presented in this paper is different from state-of-art diagnostics and resides in the pre-failure-space of the electronic-system, in which no macro-indicators such as cracks or delamination exist. Applications for the presented PHM framework include, consumer applications such as automotive safety systems including front and rear impact protection system, chassis-control systems, x-by-wire systems; and defense applications such as avionics systems, naval electronic warfare systems. The presented PHM methodologies enable the estimation of prior damage in deployed electronics by interrogation of the system state. The presented methodologies will trigger repair or replacement, significantly prior to failure. The approach involves the use of condition monitoring devices which can be interrogated for damage proxies at finite time-intervals. The system’s residual life is computed based on residual-life computation algorithms. Previously, Lall, et. al. [2004, 2005, 2006] have developed several leading indicators of failure. In this paper a mathematical approach has been presented to calculate the prior damage in electronics subjected to cyclic and isothermal thermo-mechanical loads. Electronic components operating in a harsh environment may be subjected to both temperature variations in addition to thermal aging during use-life. Data has been collected for leading indicators of failure for 95.5Sn4Ag0.5Cu first-level interconnects under both single and sequential application of cyclic and isothermal thermo-mechanical loads. Methodology for the determination of prior damage history has been presented using non-linear least-squares method based interrogation techniques. The methodology presented used the Levenberg-Marquardt Algorithm. Test vehicle includes various area-array packaging architectures soldered on Immersion Ag finish, subjected to thermal cycling in the range of −40°C to 125°C and isothermal aging at 125°C.

Experimental Analysis of a Voice-Coil-Driven Jetting System for Micrograms Fluid Depositions in Electronics Assembly

2017 ◽  
Vol 14 (3) ◽  
pp. 108-121 ◽  
Author(s):  
Rajiv L. Iyer ◽  
Daryl L. Santos
Keyword(s):  
High Speed ◽  
Printed Circuit Board ◽  
Prediction Models ◽  
Electronics Packaging ◽  
Modern Technology ◽  
Circuit Board ◽  
Electronics Assembly ◽  
Pcb Assembly ◽  
Ability To Drive ◽  
Plot Design

Abstract The fundamental ability to drive automatically to independent locations and deposit controlled masses of electronics packaging adhesives makes dispensing an attractive solution in the electronics assembly. The existence of modern technology such as smartphones, wearable devices (watches, glasses, etc.), and tablets have led to tightly spaced, and high-density component packaging which further causes complex designs in the Printed Circuit Board (PCB) assembly. Advancements in dispensing technology because of these growing challenges in assembly has led to use of jetting systems in this new arena. The process of jetting, unlike traditional dispensing, has the ability to deposit controlled masses of packaging adhesives (also known as packaging fluids) at tightly spaced locations with high accuracy and high speed and at much higher deposition heights from the substrate. In this article, a voice-coil-driven jetting system is studied to assess the capability of jetting micrograms of electronics packaging fluids. The article presents experimental analyses to study jetting of micrograms of fluid droplets. Critical input factors are evaluated using a split-plot design of experiment (DOE) model to understand their significance in governing the responses. The responses studied in this work are the following: mass per droplet, dot diameter, and dispense quality. The applied DOE model will assist in developing prediction models to determine the optimal combination of factors in achieving desired responses.

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