Data analytics approach for optimal qualification testing of electronic components

In electronics manufacturing, required quality of electronic components and parts is ensured through qualification testing using standards and user-defined requirements. The challenge for the industry is that product qualification testing is time-consuming and comes at a substantial cost. The work reported with this paper focus on the development and demonstration of a novel approach that can support “smart qualification testing” by using data analytics and data-driven prognostics modelling. Data analytics approach is developed and applied to historical qualification test datasets for an electronic module (Device under Test, DUT). The qualification spec involves a series of sequentially performed electrical and functional parameter tests on the DUTs. Data analytics is used to identify the tests that are sensitive to pending failure as well as to cross-evaluate the similarity in measurements between all tests, thus generating also knowledge on potentially redundant tests. The capability of data-driven prognostics modelling, using machine learning techniques and available historical qualification datasets, is also investigated. The results obtained from the study showed that predictive models developed from the identified so-called “sensitive to pending failure” tests feature superior performance compared with conventional, as measured, use of the test data. This work is both novel and original because at present, to the best knowledge of the authors, no similar predictive analytics methodology for qualification test time reduction (respectively cost reduction) is used in the electronics industry

Data-driven prognostics for smart qualification testing of electronic products

A flaw or drift from expected operational performance in one electronic module or component may affect the reliability of the entire upper-level electronic product or system. Therefore, it is important to ensure the required quality of each individual electronic part through qualification testing specified using standards or user requirements. Qualification testing is time-consuming and comes at a substantial cost for product manufacturers. Many electronics manufacturers have access to large historical sets of qualification testing data from their products which may hold information to enable optimization of the respective qualification procedures. In this paper, techniques from the domain of computational intelligence are applied. The development of data-driven models capable to forecast accurately and in-line the end result of a sequence of qualification tests is discussed and presented. Data-driven prognostics models are developed using test data of the electronic module by Neural Network (NN) and Support Vector Machine (SVM) techniques. The performances of the models in predicting the qualification outcomes (pass or fail) are assessed

MICROELECTRONICS PACKAGING TECHNOLOGY ROADMAPS, ASSEMBLY RELIABILITY, AND PROGNOSTICS

This paper reviews the industry roadmaps on commercial-off-the shelf (COTS) microelectronics packaging technologies covering the current trends toward further reducing size and increasing functionality. Due tothe breadth of work being performed in this field, this paper presents only a number of key packaging technologies. The topics for each category were down-selected by reviewing reports of industry roadmaps including the International Technology Roadmap for Semiconductor (ITRS) and by surveying publications of the International Electronics Manufacturing Initiative (iNEMI) and the roadmap of association connecting electronics industry (IPC). The paper also summarizes the findings of numerous articles and websites that allotted to the emerging and trends in microelectronics packaging technologies. A brief discussion was presented on packaging hierarchy from die to package and to system levels. Key elements of reliability for packaging assemblies were presented followed by reliabilty definition from a probablistic failure perspective. An example was present for showing conventional reliability approach using Monte Carlo simulation results for a number of plastic ball grid array (PBGA). The simulation results were compared to experimental thermal cycle test data. Prognostic health monitoring (PHM) methods, a growing field for microelectronics packaging technologies, were briefly discussed. The artificial neural network (ANN), a data-driven PHM, was discussed in details. Finally, it presented inter- and extra-polations using ANN simulation for thermal cycle test data of PBGA and ceramic BGA (CBGA) assemblies

Prognostics and health management of light emitting diodes

Prognostics is an engineering process of diagnosing, predicting the remaining useful life and estimating the reliability of systems and products. Prognostics and Health Management (PHM) has emerged in the last decade as one of the most efficient approaches in failure prevention, reliability estimation and remaining useful life predictions of various engineering systems and products. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incandescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. Even though LEDs have high reliability and long life time, manufacturers and lighting systems designers still need to assess the reliability of LED lighting systems and the failures in the LED. This research provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED lighting system after their deployment based on the power of the light output emitted. The data driven techniques are only based on monitoring selected operational and performance indicators using sensors whereas the model driven technique is based on sensor data as well as on a developed empirical model. Fusion approach is also developed using the data driven and the model driven approaches to the LED. Real-time implementation of developed approaches are also investigated and discussed

Predictive analytics methodology for smart qualification testing of electronic components

In electronics manufacturing, the required quality of electronic modules (e.g. packaged electronic devices) are evaluated through qualification testing using standards and user-defined requirements. The challenge for the electronics industry is that product qualification testing is time-consuming and costly. This paper focuses on the development and demonstration of a novel approach for smarter qualification using test data from the production line along with integrated computational techniques for data mining/analytics and data-driven forecasting (i.e. prognostics) modelling. The most common type of testing in the electronics industry - sequentially run electrical multi-parameter tests on the Device-under-Test (DUT), is considered. The proposed data mining (DM) framework can identify the tests that have strong correlation to pending failure of the device in the qualification (tests sensitive to pending failure) as well as to evaluate the similarity in test measurements, thus generating knowledge on potentially redundant tests. Mining the data in this context and with the proposed approach represents a major new contribution because it uncovers embedded knowledge and information in the production test data that can enable intelligent optimisation of the tests’ sequence and reduce the number of tests. The intelligent manufacturing concept behind the development of data-driven prognostics models using machine learning (ML) techniques is to use data only from a small number of tests from the full qualification specification as training data in the process of model construction. This model can then forecast the overall qualification outcome for a DUT - Pass or Fail - without performing all other remaining tests. The novelty in the context of machine learning is in the selection of the data features for the training dataset using results from tests sensitive to pending failure. Support Vector Machine (SVM) binary classifiers SVM models built with data from tests sensitive to the outcome that the module will fail are shown to have superior performance compared with models trained with other datasets of tests. Case studies based on the use of real industrial production test data for an electronic module are included in the paper to demonstrate and validate the computational approach. This work is both novel and original because at present, to the best knowledge of the authors, such predictive analytics methodology applied to qualification testing and providing benefits of test time and hence cost reduction are non-existent in the electronics industry. The integrated data analytics-prognostics approach, deployable for both off-line and in-line optimisation of production test procedures, has the potential to transform current practices by exploiting in a smarter way information and knowledge available with large datasets of qualification test data

Prognostics and Health Monitoring for ECU Based on Piezoresistive Sensor Measurements

This dissertation presents a new approach to prognostics and health monitoring for automotive applications using a piezoresistive silicon stress sensor. The stress sensor is a component with promising performance for monitoring the condition of an electronic system, as it is able to measure stress values that can be directly related to the damage sustained by the system. The primary challenge in this study is to apply a stress sensor to system-level monitoring. To achieve this goal, this study firstly evaluates the uncertainties of measurement conducted with the sensor, and then the study develops a reliable solution for gathering data with a large number of sensors. After overcoming these preliminary challenges, the study forms a framework for monitoring an electronic system with a piezoresistive stress sensor. Following this, an approach to prognostics and health monitoring involving this sensor is established. Specifically, the study chooses to use a fusion approach, which includes both model-based and data-driven approaches to prognostics; such an approach minimizes the drawbacks of using these methods separately. As the first step, the physics of failure model for the investigated product is established. The process of physics of failure model development is supported by a detailed numerical analysis of the investigated product under both active and passive thermal loading. Accurate FEM modeling provides valuable insight into the product behavior and enables quantitative evaluation of loads acting in the considered design elements. Then, a real-time monitoring of the investigated product under given loading conditions is realized to enable the system to estimate the remaining useful life based on the existing model. However, the load in the design element may abruptly change when delamination occurs. A developed data-driven approach focuses on delamination detection based on a monitoring signal. The data driven methodology utilizes statistical pattern recognition methods in order to ensure damage detection in an automatic and reliable manner. Finally, a way to combine the developed physics-of-failure and data-driven approaches is proposed, thus creating fusion approach to prognostics and health monitoring based on piezoresistive stress sensor measurements

Lifecycle Prognostics Architecture for Selected High-Cost Active Components

There are an extensive body of knowledge and some commercial products available for calculating prognostics, remaining useful life, and damage index parameters. The application of these technologies within the nuclear power community is still in its infancy. Online monitoring and condition-based maintenance is seeing increasing acceptance and deployment, and these activities provide the technological bases for expanding to add predictive/prognostics capabilities. In looking to deploy prognostics there are three key aspects of systems that are presented and discussed: (1) component/system/structure selection, (2) prognostic algorithms, and (3) prognostics architectures. Criteria are presented for component selection: feasibility, failure probability, consequences of failure, and benefits of the prognostics and health management (PHM) system. The basis and methods commonly used for prognostics algorithms are reviewed and summarized. Criteria for evaluating PHM architectures are presented: open, modular architecture; platform independence; graphical user interface for system development and/or results viewing; web enabled tools; scalability; and standards compatibility. Thirteen software products were identified and discussed in the context of being potentially useful for deployment in a PHM program applied to systems in a nuclear power plant (NPP). These products were evaluated by using information available from company websites, product brochures, fact sheets, scholarly publications, and direct communication with vendors. The thirteen products were classified into four groups of software: (1) research tools, (2) PHM system development tools, (3) deployable architectures, and (4) peripheral tools. Eight software tools fell into the deployable architectures category. Of those eight, only two employ all six modules of a full PHM system. Five systems did not offer prognostic estimates, and one system employed the full health monitoring suite but lacked operations and maintenance support. Each product is briefly described in Appendix A. Selection of the most appropriate software package for a particular application will depend on the chosen component, system, or structure. Ongoing research will determine the most appropriate choices for a successful demonstration of PHM systems in aging NPPs

ELECTRONIC PROGNOSTICS AND HEALTH MANAGEMENT: A RETURN ON INVESTMENT ANALYSIS

Prognostics and Health Management (PHM) provides the potential to lower sustainment costs, to improve maintenance decision-making, and to provide product usage feedback into the product design and validation process. A case analysis was developed using a discrete event simulation to determine the benefits and the potential cost avoidance resulting from the use of PHM in avionics. The model allows for variability in implementation costs, operational profile, false alarms, random failure rates, and system composition to enable a comprehensive calculation of the Return on Investment (ROI) in support of acquisition decision making. The case analysis compared the life cycle costs using unscheduled maintenance to the life cycle costs using two types of PHM approaches