Test Set Optimization by Machine Learning Algorithms
This work addresses efficiency in circuit testing, but it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackled the problem of optimizing test set volume for circuit diagnosis by predicting the minimum test data needed for accurate results, achieving a 90.4% diagnosis accuracy with a 35.24% reduction in test set size using SVM.
Diagnosis results are highly dependent on the volume of test set. To derive the most efficient test set, we propose several machine learning based methods to predict the minimum amount of test data that produces relatively accurate diagnosis. By collecting outputs from failing circuits, the feature matrix and label vector are generated, which involves the inference information of the test termination point. Thus we develop a prediction model to fit the data and determine when to terminate testing. The considered methods include LASSO and Support Vector Machine(SVM) where the relationship between goals(label) and predictors(feature matrix) are considered to be linear in LASSO and nonlinear in SVM. Numerical results show that SVM reaches a diagnosis accuracy of 90.4% while deducting the volume of test set by 35.24%.