Test Set Optimization by Machine Learning Algorithms

TL;DR

This paper introduces machine learning methods, including LASSO and SVM, to optimize test set size for circuit diagnosis, achieving high accuracy with reduced testing volume.

Contribution

It proposes a novel approach using ML algorithms to predict minimal test data needed for accurate diagnosis, improving efficiency over traditional methods.

Findings

SVM achieves 90.4% diagnosis accuracy.

Test set volume is reduced by 35.24%.

ML-based prediction effectively optimizes testing process.

Abstract

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%.