Fine-Grained Adaptive Testing Based on Quality Prediction

Conference Paper

The ever-increasing complexity of integrated circuits inevitably leads to high test cost. Adaptive testing provides an effective solution for test-cost reduction; this testing framework selects the important test items for each set of chips. However, adaptive testing methods designed for digital circuits are coarse-grained, and they are targeted only at systematic defects. In order to incorporate fabrication variations and random defects in the testing framework, we propose a fine-grained adaptive testing method based on machine learning. We use the parametric test results from the previous stages of test to train a quality-prediction model for use in subsequent test stages. Next, we partition a given lot of chips into two groups based on their predicted quality. A test-selection method based on statistical learning is applied to the chips with high predicted quality. An ad hoc test-selection method is proposed and applied to the chips with low predicted quality. Experimental results using a large number of fabricated chips and the associated test data show that to achieve the same defect level as in prior work on adaptive testing, the fine-grained adaptive testing method reduces test cost by 90% for low-quality chips, and up to 7% for all the chips in a lot.

Full Text

Duke Authors

Cited Authors

  • Liu, M; Pan, R; Ye, F; Li, X; Chakrabarty, K; Gu, X

Published Date

  • January 23, 2019

Published In

Volume / Issue

  • 2018-October /

International Standard Serial Number (ISSN)

  • 1089-3539

International Standard Book Number 13 (ISBN-13)

  • 9781538683828

Digital Object Identifier (DOI)

  • 10.1109/TEST.2018.8624891

Citation Source

  • Scopus