Wafer-level Adaptive Testing Based on Dual-Predictor Collaborative Decision

The growing complexity of integrated circuits (ICs) brings expensive manufacturing test cost. Adaptive testing becomes an important way to save test cost by predicting die quality to reduce the actual test content. However, reducing test items often results in unacceptable levels of test quality degradation. An adaptive testing method is proposed in the paper to reduce test cost while guaranteeing test quality. Two quality predictors are trained with a subset of test items and spatial information for subsequent decisions. The dies are clustered according to the prediction results, and the clustering results are graded. The distribution of the different grade classes determines the die quality of each grade. Experimental results using fabricated wafers and the associated test data show that the proposed method reduces more than 42% of test items, and can achieve better test quality, reducing test escapes and yield losses by more than 90% in the Circuit Probing test (CP).