Accurate Estimation of Test Pattern Counts for a Wide-Range of EDT Input/Output Channel Configurations

Shi Xuan Zheng, Chung Yu Yeh, Kuen Jong Lee, Chen Wang, Wu Tung Cheng, Mark Kassab, Janusz Rajski, Sudhakar M. Reddy

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

Abstract

Test cost has become a critical issue for large industrial integrated circuits. Various test compression techniques have been adopted in the industry to reduce test cost. However, appropriate input and output channel counts must be selected to utilize the test compression technology best. This paper presents an efficient and effective method to estimate the test pattern counts under different compression configurations for the Embedded Deterministic Test (EDT) compression technique. In searching for the accurate estimation method, we build mathematical models that reveal the internal relationship among different compression configurations. The models are established based on novel theoretical analysis as well as actual experimental data. Accurate estimation of test pattern counts for a wide range of compression configurations can be obtained based on the results of only two ATPG runs. Experimental results on nine industrial circuits show that the average error rate of pattern count estimation is about 5%, with very few outliers. With the proposed method, a test compression designer can easily pick the best input and output channel configuration to fit the design needs.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 40th VLSI Test Symposium, VTS 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781665410601
DOIs
Publication statusPublished - 2022
Event40th IEEE VLSI Test Symposium, VTS 2022 - Virtual, Online, United States
Duration: 2022 Apr 252022 Apr 27

Publication series

NameProceedings of the IEEE VLSI Test Symposium
Volume2022-April

Conference

Conference40th IEEE VLSI Test Symposium, VTS 2022
Country/TerritoryUnited States
CityVirtual, Online
Period22-04-2522-04-27

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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