Predictive analytics methodology for smart qualification testing of electronic components

Abstract

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 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.

A Discrete Multi Objective Artificial Bee Colony Algorithm for a Real World Electronic Device Testing Machine Allocation Problem

“…Constraint (3) computes the weekly production of a testing machine at each stage. Constraint (4) ensures that only machines with the interfaces matching a stage can be scheduled to the stage. Constraint…”

“…The system efficiently tested the electronic device by measuring its surface temperature. Stoyanov et al [4] developed a smart qualification testing approach to test electronic devices. The method combined data analytics and datadriven predictive modeling to reduce testing time and cost.…”

Developing Computational Intelligence for Smart Qualification Testing of Electronic Products

“…Data-driven prognostics models were developed using test data for forecasting anticipated qualification tests outcome. Offline smart tests was accomplished through failure statistics and similarity tests and details of the tests data analytics are presented in [18]. This paper presents an offline analytics to develop prognostic models for forecasting the overall test outcome by evaluating model performances following five individual steps.…”

“…The main advantage of this technique is that one rule can be applied across the whole range of tests. Further details of normalization can be found in [18].…”

“…In the authors' previous work, an Off-line data analytics and imbedded in-line model-enabled prognostics approach has been proposed for smart qualification test of an electronic product using SVM [18]. The aim of this study is to develop a machine learning based novel computational approach for predicting qualification test outcomes of an electronic module using historical test data.…”