Online information utility assessment for per-device adaptive test flow

Yanjun Li, Ender Yilmaz, Peter Sarson, Sule Ozev

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Per-device adaptive test is a promising direction with the best trade-off between test quality and test time so far. In this work, we propose a method for online assessment of the information content of the next test in the test queue. This assessment can be used to tune the trade-off between test quality and test time of a per-device adaptive test. Since majority of specification parameters are correlated, the overall information content of multiple tests is difficult to extract. We model multi-variate correlations among specification parameters and take these correlations into account to estimate the multivariate overall information utility of a given set of tests. The proposed method can be integrated within an existing adaptive test flow (per-device or per-wafer) that runs in the background. Experimental results using 3 distinct industry circuits and sizable data show that the proposed technique can finely tune the trade-off, even achieve zero test escape rates with appreciable test time savings.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE 36th VLSI Test Symposium, VTS 2018
PublisherIEEE Computer Society
Pages1-6
Number of pages6
Volume2018-April
ISBN (Electronic)9781538637746
DOIs
StatePublished - May 29 2018
Event36th IEEE VLSI Test Symposium, VTS 2018 - San Francisco, United States
Duration: Apr 22 2018Apr 25 2018

Other

Other36th IEEE VLSI Test Symposium, VTS 2018
CountryUnited States
CitySan Francisco
Period4/22/184/25/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Li, Y., Yilmaz, E., Sarson, P., & Ozev, S. (2018). Online information utility assessment for per-device adaptive test flow. In Proceedings - 2018 IEEE 36th VLSI Test Symposium, VTS 2018 (Vol. 2018-April, pp. 1-6). IEEE Computer Society. https://doi.org/10.1109/VTS.2018.8368662